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evaluation_data/wire57/gold_data/gold_wire57_test.tsv
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| 1 |
+
Tokyo (ˈtoʊkioʊ, Japanese: toːkʲoː), officially Tokyo Metropolis, is the capital city of Japan and one of its 47 prefectures. is in Japanese Tokyo toːkʲoː
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| 2 |
+
Tokyo (ˈtoʊkioʊ, Japanese: toːkʲoː), officially Tokyo Metropolis, is the capital city of Japan and one of its 47 prefectures. is pronounced Tokyo ˈtoʊkioʊ
|
| 3 |
+
Tokyo (ˈtoʊkioʊ, Japanese: toːkʲoː), officially Tokyo Metropolis, is the capital city of Japan and one of its 47 prefectures. is officially Tokyo Tokyo Metropolis
|
| 4 |
+
Tokyo (ˈtoʊkioʊ, Japanese: toːkʲoː), officially Tokyo Metropolis, is the capital city of Japan and one of its 47 prefectures. is the capital city of Tokyo Japan
|
| 5 |
+
Tokyo (ˈtoʊkioʊ, Japanese: toːkʲoː), officially Tokyo Metropolis, is the capital city of Japan and one of its 47 prefectures. is Tokyo the capital city of Japan
|
| 6 |
+
Tokyo (ˈtoʊkioʊ, Japanese: toːkʲoː), officially Tokyo Metropolis, is the capital city of Japan and one of its 47 prefectures. is one of Tokyo its prefectures
|
| 7 |
+
Tokyo (ˈtoʊkioʊ, Japanese: toːkʲoː), officially Tokyo Metropolis, is the capital city of Japan and one of its 47 prefectures. is one of Tokyo its 47 prefectures
|
| 8 |
+
Tokyo (ˈtoʊkioʊ, Japanese: toːkʲoː), officially Tokyo Metropolis, is the capital city of Japan and one of its 47 prefectures. is a prefecture of Tokyo Japan
|
| 9 |
+
Tokyo (ˈtoʊkioʊ, Japanese: toːkʲoː), officially Tokyo Metropolis, is the capital city of Japan and one of its 47 prefectures. has Japan 47 prefectures
|
| 10 |
+
Tokyo (ˈtoʊkioʊ, Japanese: toːkʲoː), officially Tokyo Metropolis, is the capital city of Japan and one of its 47 prefectures. is Tokyo a prefecture
|
| 11 |
+
Tokyo (ˈtoʊkioʊ, Japanese: toːkʲoː), officially Tokyo Metropolis, is the capital city of Japan and one of its 47 prefectures. is Tokyo a city
|
| 12 |
+
The Greater Tokyo Area is the most populous metropolitan area in the world. is The Greater Tokyo Area the most populous metropolitan area
|
| 13 |
+
The Greater Tokyo Area is the most populous metropolitan area in the world. is The Greater Tokyo Area a metropolitan area
|
| 14 |
+
It is the seat of the Emperor of Japan and the Japanese government. is the seat of It the Emperor of Japan
|
| 15 |
+
It is the seat of the Emperor of Japan and the Japanese government. is the seat of It the Japanese government
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| 16 |
+
It is the seat of the Emperor of Japan and the Japanese government. has Japan an Emperor
|
| 17 |
+
It is the seat of the Emperor of Japan and the Japanese government. has Japan a government
|
| 18 |
+
Tokyo is in the Kantō region on the southeastern side of the main island Honshu and includes the Izu Islands and Ogasawara Islands. is in Tokyo the Kantō region
|
| 19 |
+
Tokyo is in the Kantō region on the southeastern side of the main island Honshu and includes the Izu Islands and Ogasawara Islands. is in Tokyo Kantō
|
| 20 |
+
Tokyo is in the Kantō region on the southeastern side of the main island Honshu and includes the Izu Islands and Ogasawara Islands. is Kantō a region
|
| 21 |
+
Tokyo is in the Kantō region on the southeastern side of the main island Honshu and includes the Izu Islands and Ogasawara Islands. is on Tokyo the southeastern side of the main island Honshu
|
| 22 |
+
Tokyo is in the Kantō region on the southeastern side of the main island Honshu and includes the Izu Islands and Ogasawara Islands. is on the Kantō region the southeastern side of the main island Honshu
|
| 23 |
+
Tokyo is in the Kantō region on the southeastern side of the main island Honshu and includes the Izu Islands and Ogasawara Islands. is Honshu an island
|
| 24 |
+
Tokyo is in the Kantō region on the southeastern side of the main island Honshu and includes the Izu Islands and Ogasawara Islands. is Honshu the main island
|
| 25 |
+
Tokyo is in the Kantō region on the southeastern side of the main island Honshu and includes the Izu Islands and Ogasawara Islands. includes Tokyo the Izu Islands
|
| 26 |
+
Tokyo is in the Kantō region on the southeastern side of the main island Honshu and includes the Izu Islands and Ogasawara Islands. includes Tokyo the Ogasawara Islands
|
| 27 |
+
Tokyo is in the Kantō region on the southeastern side of the main island Honshu and includes the Izu Islands and Ogasawara Islands. has Tokyo islands
|
| 28 |
+
Tokyo is in the Kantō region on the southeastern side of the main island Honshu and includes the Izu Islands and Ogasawara Islands. has Honshu a southeastern side
|
| 29 |
+
Formerly known as Edo, it has been the de facto seat of government since 1603 when Shogun Tokugawa Ieyasu made the city his headquarters. was Formerly known as it Edo
|
| 30 |
+
Formerly known as Edo, it has been the de facto seat of government since 1603 when Shogun Tokugawa Ieyasu made the city his headquarters. has been it the seat of government
|
| 31 |
+
Formerly known as Edo, it has been the de facto seat of government since 1603 when Shogun Tokugawa Ieyasu made the city his headquarters. made Tokugawa Ieyasu the city
|
| 32 |
+
Formerly known as Edo, it has been the de facto seat of government since 1603 when Shogun Tokugawa Ieyasu made the city his headquarters. is Tokugawa Ieyasu Shogun
|
| 33 |
+
Formerly known as Edo, it has been the de facto seat of government since 1603 when Shogun Tokugawa Ieyasu made the city his headquarters. is it the seat of government
|
| 34 |
+
Formerly known as Edo, it has been the de facto seat of government since 1603 when Shogun Tokugawa Ieyasu made the city his headquarters. has Tokugawa Ieyasu headquarters
|
| 35 |
+
It officially became the capital after Emperor Meiji moved his seat to the city from the old capital of Kyoto in 1868; at that time Edo was renamed Tokyo. became It the capital
|
| 36 |
+
It officially became the capital after Emperor Meiji moved his seat to the city from the old capital of Kyoto in 1868; at that time Edo was renamed Tokyo. moved Emperor Meiji his seat
|
| 37 |
+
It officially became the capital after Emperor Meiji moved his seat to the city from the old capital of Kyoto in 1868; at that time Edo was renamed Tokyo. was renamed Edo Tokyo
|
| 38 |
+
It officially became the capital after Emperor Meiji moved his seat to the city from the old capital of Kyoto in 1868; at that time Edo was renamed Tokyo. is Kyoto the old capital
|
| 39 |
+
Tokyo Metropolis was formed in 1943 from the merger of the former Tokyo Prefecture (東京府 Tōkyō-fu) and the city of Tokyo (東京市 Tōkyō-shi). was formed in Tokyo Metropolis 1943
|
| 40 |
+
Tokyo Metropolis was formed in 1943 from the merger of the former Tokyo Prefecture (東京府 Tōkyō-fu) and the city of Tokyo (東京市 Tōkyō-shi). was formed from Tokyo Metropolis the merger of the Tokyo Prefecture and the city of Tokyo
|
| 41 |
+
Tokyo Metropolis was formed in 1943 from the merger of the former Tokyo Prefecture (東京府 Tōkyō-fu) and the city of Tokyo (東京市 Tōkyō-shi). is the city of Tokyo Tōkyō-shi
|
| 42 |
+
Tokyo Metropolis was formed in 1943 from the merger of the former Tokyo Prefecture (東京府 Tōkyō-fu) and the city of Tokyo (東京市 Tōkyō-shi). is the city of Tokyo 東京市
|
| 43 |
+
Tokyo Metropolis was formed in 1943 from the merger of the former Tokyo Prefecture (東京府 Tōkyō-fu) and the city of Tokyo (東京市 Tōkyō-shi). is the former Tokyo Prefecture 東京府
|
| 44 |
+
Tokyo Metropolis was formed in 1943 from the merger of the former Tokyo Prefecture (東京府 Tōkyō-fu) and the city of Tokyo (東京市 Tōkyō-shi). is the former Tokyo Prefecture Tōkyō-fu
|
| 45 |
+
Tokyo Metropolis was formed in 1943 from the merger of the former Tokyo Prefecture (東京府 Tōkyō-fu) and the city of Tokyo (東京市 Tōkyō-shi). merged with the former Tokyo Prefecture the city of Tokyo
|
| 46 |
+
Tokyo is often referred to as a city, but is officially known and governed as a "metropolitan prefecture", which differs from and combines elements of a city and a prefecture, a characteristic unique to Tokyo. is referred to as Tokyo a city
|
| 47 |
+
Tokyo is often referred to as a city, but is officially known and governed as a "metropolitan prefecture", which differs from and combines elements of a city and a prefecture, a characteristic unique to Tokyo. is officially known as Tokyo a `` metropolitan prefecture ''
|
| 48 |
+
Tokyo is often referred to as a city, but is officially known and governed as a "metropolitan prefecture", which differs from and combines elements of a city and a prefecture, a characteristic unique to Tokyo. is governed as Tokyo a `` metropolitan prefecture ''
|
| 49 |
+
Tokyo is often referred to as a city, but is officially known and governed as a "metropolitan prefecture", which differs from and combines elements of a city and a prefecture, a characteristic unique to Tokyo. differs from a `` metropolitan prefecture '' a city
|
| 50 |
+
Tokyo is often referred to as a city, but is officially known and governed as a "metropolitan prefecture", which differs from and combines elements of a city and a prefecture, a characteristic unique to Tokyo. differs from a `` metropolitan prefecture '' a prefecture
|
| 51 |
+
Tokyo is often referred to as a city, but is officially known and governed as a "metropolitan prefecture", which differs from and combines elements of a city and a prefecture, a characteristic unique to Tokyo. combines elements of a `` metropolitan prefecture '' a city and a prefecture
|
| 52 |
+
Tokyo is often referred to as a city, but is officially known and governed as a "metropolitan prefecture", which differs from and combines elements of a city and a prefecture, a characteristic unique to Tokyo. is unique to a characteristic Tokyo
|
| 53 |
+
The Tokyo metropolitan government administers the 23 Special Wards of Tokyo (each governed as an individual city), which cover the area that was the City of Tokyo before it merged and became the metropolitan prefecture in 1943. administers The Tokyo metropolitan government the 23 Special Wards of Tokyo
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| 54 |
+
The Tokyo metropolitan government administers the 23 Special Wards of Tokyo (each governed as an individual city), which cover the area that was the City of Tokyo before it merged and became the metropolitan prefecture in 1943. has Tokyo 23 Special Wards
|
| 55 |
+
The Tokyo metropolitan government administers the 23 Special Wards of Tokyo (each governed as an individual city), which cover the area that was the City of Tokyo before it merged and became the metropolitan prefecture in 1943. is governed as each an individual city
|
| 56 |
+
The Tokyo metropolitan government administers the 23 Special Wards of Tokyo (each governed as an individual city), which cover the area that was the City of Tokyo before it merged and became the metropolitan prefecture in 1943. merged the City of Tokyo in 1943
|
| 57 |
+
The Tokyo metropolitan government administers the 23 Special Wards of Tokyo (each governed as an individual city), which cover the area that was the City of Tokyo before it merged and became the metropolitan prefecture in 1943. became the City of Tokyo the metropolitan prefecture
|
| 58 |
+
The Tokyo metropolitan government administers the 23 Special Wards of Tokyo (each governed as an individual city), which cover the area that was the City of Tokyo before it merged and became the metropolitan prefecture in 1943. has Tokyo a metropolitan government
|
| 59 |
+
The Tokyo metropolitan government administers the 23 Special Wards of Tokyo (each governed as an individual city), which cover the area that was the City of Tokyo before it merged and became the metropolitan prefecture in 1943. is the City of Tokyo an area
|
| 60 |
+
The metropolitan government also administers 39 municipalities in the western part of the prefecture and the two outlying island chains. administers The metropolitan government 39 municipalities
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| 61 |
+
The metropolitan government also administers 39 municipalities in the western part of the prefecture and the two outlying island chains. administers The metropolitan government the two outlying island chains
|
| 62 |
+
The metropolitan government also administers 39 municipalities in the western part of the prefecture and the two outlying island chains. has the prefecture a western part
|
| 63 |
+
The metropolitan government also administers 39 municipalities in the western part of the prefecture and the two outlying island chains. are the two island chains outliers
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| 64 |
+
The population of the special wards is over 9 million people, with the total population of the prefecture exceeding 13 million. is over The population of the special wards 9 million people
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| 65 |
+
The population of the special wards is over 9 million people, with the total population of the prefecture exceeding 13 million. exceeds the total population of the prefecture 13 million
|
| 66 |
+
The population of the special wards is over 9 million people, with the total population of the prefecture exceeding 13 million. has the prefecture a population
|
| 67 |
+
The population of the special wards is over 9 million people, with the total population of the prefecture exceeding 13 million. have the special wards a population
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| 68 |
+
The prefecture is part of the world's most populous metropolitan area with upwards of 37.8 million people and the world's largest urban agglomeration economy. is part of The prefecture the world 's most populous metropolitan area
|
| 69 |
+
The prefecture is part of the world's most populous metropolitan area with upwards of 37.8 million people and the world's largest urban agglomeration economy. has upwards of the world 's most populous metropolitan area 37.8 million people
|
| 70 |
+
The prefecture is part of the world's most populous metropolitan area with upwards of 37.8 million people and the world's largest urban agglomeration economy. is part of The prefecture the world 's largest urban agglomeration economy
|
| 71 |
+
The prefecture is part of the world's most populous metropolitan area with upwards of 37.8 million people and the world's largest urban agglomeration economy. has the world metropolitan areas
|
| 72 |
+
The prefecture is part of the world's most populous metropolitan area with upwards of 37.8 million people and the world's largest urban agglomeration economy. has the world urban agglomeration economies
|
| 73 |
+
The prefecture is part of the world's most populous metropolitan area with upwards of 37.8 million people and the world's largest urban agglomeration economy. has the world a most populous metropolitan area
|
| 74 |
+
The prefecture is part of the world's most populous metropolitan area with upwards of 37.8 million people and the world's largest urban agglomeration economy. has the world a largest urban agglomeration economy
|
| 75 |
+
The prefecture is part of the world's most populous metropolitan area with upwards of 37.8 million people and the world's largest urban agglomeration economy. have urban agglomerations economies
|
| 76 |
+
In 2011, the city hosted 51 of the Fortune Global 500 companies, the highest number of any city in the world, at that time. hosted the city 51 of the Fortune Global 500 companies
|
| 77 |
+
In 2011, the city hosted 51 of the Fortune Global 500 companies, the highest number of any city in the world, at that time. hosted the city the highest number of any city in the world
|
| 78 |
+
In 2011, the city hosted 51 of the Fortune Global 500 companies, the highest number of any city in the world, at that time. hosted the city companies
|
| 79 |
+
In 2011, the city hosted 51 of the Fortune Global 500 companies, the highest number of any city in the world, at that time. has any city a number of Fortune Global 500 companies
|
| 80 |
+
Tokyo ranked third (twice) in the International Financial Centres Development IndexEdit. ranked Tokyo third
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| 81 |
+
The city is also home to various television networks such as Fuji TV, Tokyo MX, TV Tokyo, TV Asahi, Nippon Television, NHK and the Tokyo Broadcasting System. is home to The city various television networks
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| 82 |
+
The city is also home to various television networks such as Fuji TV, Tokyo MX, TV Tokyo, TV Asahi, Nippon Television, NHK and the Tokyo Broadcasting System. is home to The city Fuji TV
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| 83 |
+
The city is also home to various television networks such as Fuji TV, Tokyo MX, TV Tokyo, TV Asahi, Nippon Television, NHK and the Tokyo Broadcasting System. is Fuji TV a television network
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| 84 |
+
The city is also home to various television networks such as Fuji TV, Tokyo MX, TV Tokyo, TV Asahi, Nippon Television, NHK and the Tokyo Broadcasting System. is home to The city Tokyo MX
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| 85 |
+
The city is also home to various television networks such as Fuji TV, Tokyo MX, TV Tokyo, TV Asahi, Nippon Television, NHK and the Tokyo Broadcasting System. is Tokyo MX a television network
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| 86 |
+
The city is also home to various television networks such as Fuji TV, Tokyo MX, TV Tokyo, TV Asahi, Nippon Television, NHK and the Tokyo Broadcasting System. is home to The city TV Tokyo
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| 87 |
+
The city is also home to various television networks such as Fuji TV, Tokyo MX, TV Tokyo, TV Asahi, Nippon Television, NHK and the Tokyo Broadcasting System. is TV Tokyo a television network
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| 88 |
+
The city is also home to various television networks such as Fuji TV, Tokyo MX, TV Tokyo, TV Asahi, Nippon Television, NHK and the Tokyo Broadcasting System. is home to The city TV Asahi
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| 89 |
+
The city is also home to various television networks such as Fuji TV, Tokyo MX, TV Tokyo, TV Asahi, Nippon Television, NHK and the Tokyo Broadcasting System. is TV Asahi a television network
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| 90 |
+
The city is also home to various television networks such as Fuji TV, Tokyo MX, TV Tokyo, TV Asahi, Nippon Television, NHK and the Tokyo Broadcasting System. is home to The city Nippon Television
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| 91 |
+
The city is also home to various television networks such as Fuji TV, Tokyo MX, TV Tokyo, TV Asahi, Nippon Television, NHK and the Tokyo Broadcasting System. is Nippon Television a television network
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| 92 |
+
The city is also home to various television networks such as Fuji TV, Tokyo MX, TV Tokyo, TV Asahi, Nippon Television, NHK and the Tokyo Broadcasting System. is home to The city NHK
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| 93 |
+
The city is also home to various television networks such as Fuji TV, Tokyo MX, TV Tokyo, TV Asahi, Nippon Television, NHK and the Tokyo Broadcasting System. is NHK a television network
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| 94 |
+
The city is also home to various television networks such as Fuji TV, Tokyo MX, TV Tokyo, TV Asahi, Nippon Television, NHK and the Tokyo Broadcasting System. is home to The city the Tokyo Broadcasting System
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| 95 |
+
The city is also home to various television networks such as Fuji TV, Tokyo MX, TV Tokyo, TV Asahi, Nippon Television, NHK and the Tokyo Broadcasting System. is the Tokyo Broadcasting System a television network
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| 96 |
+
Finnish police reprimanded a man for traveling in a car boot to hide his meeting with Prime Minister Juha Sipila during a government crisis last summer, saying this was breach of the traffic code. reprimanded Finnish police a man
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| 97 |
+
Finnish police reprimanded a man for traveling in a car boot to hide his meeting with Prime Minister Juha Sipila during a government crisis last summer, saying this was breach of the traffic code. met with a man Juha Sipila
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| 98 |
+
Finnish police reprimanded a man for traveling in a car boot to hide his meeting with Prime Minister Juha Sipila during a government crisis last summer, saying this was breach of the traffic code. is Juha Sipila Prime Minister
|
| 99 |
+
Finnish police reprimanded a man for traveling in a car boot to hide his meeting with Prime Minister Juha Sipila during a government crisis last summer, saying this was breach of the traffic code. was this a breach of the traffic code
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| 100 |
+
Finnish police reprimanded a man for traveling in a car boot to hide his meeting with Prime Minister Juha Sipila during a government crisis last summer, saying this was breach of the traffic code. happened a government crisis last summer
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| 101 |
+
Finnish police reprimanded a man for traveling in a car boot to hide his meeting with Prime Minister Juha Sipila during a government crisis last summer, saying this was breach of the traffic code. had a man a meeting with Juha Sipila
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| 102 |
+
Finnish police reprimanded a man for traveling in a car boot to hide his meeting with Prime Minister Juha Sipila during a government crisis last summer, saying this was breach of the traffic code. had the government a crisis
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| 103 |
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A police statement did not name the man in the boot, but in effect indicated the traveler was State Secretary Samuli Virtanen, who is also the deputy to Foreign Minister Timo Soini. did not name A police statement the man in the boot
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| 104 |
+
A police statement did not name the man in the boot, but in effect indicated the traveler was State Secretary Samuli Virtanen, who is also the deputy to Foreign Minister Timo Soini. was the man in the boot Samuli Virtanen
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| 105 |
+
A police statement did not name the man in the boot, but in effect indicated the traveler was State Secretary Samuli Virtanen, who is also the deputy to Foreign Minister Timo Soini. was the traveler Samuli Virtanen
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| 106 |
+
A police statement did not name the man in the boot, but in effect indicated the traveler was State Secretary Samuli Virtanen, who is also the deputy to Foreign Minister Timo Soini. is Samuli Virtanen State Secretary
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| 107 |
+
A police statement did not name the man in the boot, but in effect indicated the traveler was State Secretary Samuli Virtanen, who is also the deputy to Foreign Minister Timo Soini. is Samuli Virtanen the deputy to Foreign Minister Timo Soini
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| 108 |
+
A police statement did not name the man in the boot, but in effect indicated the traveler was State Secretary Samuli Virtanen, who is also the deputy to Foreign Minister Timo Soini. is Samuli Virtanen a deputy
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| 109 |
+
A police statement did not name the man in the boot, but in effect indicated the traveler was State Secretary Samuli Virtanen, who is also the deputy to Foreign Minister Timo Soini. is Timo Soini Foreign Minister
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| 110 |
+
A police statement did not name the man in the boot, but in effect indicated the traveler was State Secretary Samuli Virtanen, who is also the deputy to Foreign Minister Timo Soini. has Timo Soini a deputy
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| 111 |
+
The meeting took place in June, a day after Virtanen’s co-ruling Finns party had elected anti-immigration hardliners as its new leaders. took place in The meeting June
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+
The meeting took place in June, a day after Virtanen’s co-ruling Finns party had elected anti-immigration hardliners as its new leaders. elected the Finns party anti-immigration hardliners
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| 113 |
+
The meeting took place in June, a day after Virtanen’s co-ruling Finns party had elected anti-immigration hardliners as its new leaders. is the Finns party co-ruling
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| 114 |
+
The meeting took place in June, a day after Virtanen’s co-ruling Finns party had elected anti-immigration hardliners as its new leaders. belongs to Virtanen the Finns party
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| 115 |
+
The meeting took place in June, a day after Virtanen’s co-ruling Finns party had elected anti-immigration hardliners as its new leaders. has the Finns party new leaders
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| 116 |
+
The meeting took place in June, a day after Virtanen’s co-ruling Finns party had elected anti-immigration hardliners as its new leaders. are anti-immigration hardliners its new leaders
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| 117 |
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The meeting took place in June, a day after Virtanen’s co-ruling Finns party had elected anti-immigration hardliners as its new leaders. has the Finns party leaders
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| 118 |
+
The government was close to collapse until a group of politicians, including Virtanen and Soini, in the following week walked out of the Finns party and announced they would form a new group. was close to The government collapse
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| 119 |
+
The government was close to collapse until a group of politicians, including Virtanen and Soini, in the following week walked out of the Finns party and announced they would form a new group. walked out of a group of politicians the Finns party
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| 120 |
+
The government was close to collapse until a group of politicians, including Virtanen and Soini, in the following week walked out of the Finns party and announced they would form a new group. walked out of Virtanen the Finns party
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| 121 |
+
The government was close to collapse until a group of politicians, including Virtanen and Soini, in the following week walked out of the Finns party and announced they would form a new group. walked out of Soini the Finns party
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| 122 |
+
The government was close to collapse until a group of politicians, including Virtanen and Soini, in the following week walked out of the Finns party and announced they would form a new group. is Virtanen a politician
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| 123 |
+
The government was close to collapse until a group of politicians, including Virtanen and Soini, in the following week walked out of the Finns party and announced they would form a new group. is Soini a politician
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| 124 |
+
The government was close to collapse until a group of politicians, including Virtanen and Soini, in the following week walked out of the Finns party and announced they would form a new group. would form a group of politicians a new group
|
| 125 |
+
The government was close to collapse until a group of politicians, including Virtanen and Soini, in the following week walked out of the Finns party and announced they would form a new group. would form a group of politicians including Virtanen and Soini a new group
|
| 126 |
+
The Finns party was thrown out of the government and the new “Blue Reform” group kept its cabinet seat. was thrown out of The Finns party the government
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| 127 |
+
The Finns party was thrown out of the government and the new “Blue Reform” group kept its cabinet seat. kept Blue Reform its cabinet seat
|
| 128 |
+
The Finns party was thrown out of the government and the new “Blue Reform” group kept its cabinet seat. kept the new group its cabinet seat
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| 129 |
+
The Finns party was thrown out of the government and the new “Blue Reform” group kept its cabinet seat. is Blue Reform a group
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| 130 |
+
Virtanen has not commented on the case, but lawmaker Tiina Elovaara from Blue Reform said in a blog that Virtanen climbed into the boot to keep the meeting secret at a critical moment. has not commented on Virtanen the case
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| 131 |
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Virtanen has not commented on the case, but lawmaker Tiina Elovaara from Blue Reform said in a blog that Virtanen climbed into the boot to keep the meeting secret at a critical moment. is Tiina Elovaara a lawmaker
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| 132 |
+
Virtanen has not commented on the case, but lawmaker Tiina Elovaara from Blue Reform said in a blog that Virtanen climbed into the boot to keep the meeting secret at a critical moment. is from Tiina Elovaara Blue Reform
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| 133 |
+
Virtanen has not commented on the case, but lawmaker Tiina Elovaara from Blue Reform said in a blog that Virtanen climbed into the boot to keep the meeting secret at a critical moment. climbed into Virtanen the boot
|
| 134 |
+
“He avoided media attention when the situation was most serious, and the risk of leakage about the parliamentarians’ transition was too big,” Elovaara said. avoided He media attention
|
| 135 |
+
“He avoided media attention when the situation was most serious, and the risk of leakage about the parliamentarians’ transition was too big,” Elovaara said. was the risk of leakage about the parliamentarians ’ transition too big
|
| 136 |
+
Chilly Gonzales (born Jason Charles Beck; 20 March 1972) is a Grammy-winning Canadian musician who resided in Paris, France for several years, and now lives in Cologne, Germany. was born Chilly Gonzales Jason Charles Beck
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| 137 |
+
Chilly Gonzales (born Jason Charles Beck; 20 March 1972) is a Grammy-winning Canadian musician who resided in Paris, France for several years, and now lives in Cologne, Germany. was born on Chilly Gonzales 20 March 1972
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| 138 |
+
Chilly Gonzales (born Jason Charles Beck; 20 March 1972) is a Grammy-winning Canadian musician who resided in Paris, France for several years, and now lives in Cologne, Germany. was born in Chilly Gonzales 1972
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| 139 |
+
Chilly Gonzales (born Jason Charles Beck; 20 March 1972) is a Grammy-winning Canadian musician who resided in Paris, France for several years, and now lives in Cologne, Germany. is Chilly Gonzales Canadian
|
| 140 |
+
Chilly Gonzales (born Jason Charles Beck; 20 March 1972) is a Grammy-winning Canadian musician who resided in Paris, France for several years, and now lives in Cologne, Germany. won Chilly Gonzales a Grammy
|
| 141 |
+
Chilly Gonzales (born Jason Charles Beck; 20 March 1972) is a Grammy-winning Canadian musician who resided in Paris, France for several years, and now lives in Cologne, Germany. is Chilly Gonzales a musician
|
| 142 |
+
Chilly Gonzales (born Jason Charles Beck; 20 March 1972) is a Grammy-winning Canadian musician who resided in Paris, France for several years, and now lives in Cologne, Germany. resided in who Paris , France
|
| 143 |
+
Chilly Gonzales (born Jason Charles Beck; 20 March 1972) is a Grammy-winning Canadian musician who resided in Paris, France for several years, and now lives in Cologne, Germany. resided in who France
|
| 144 |
+
Chilly Gonzales (born Jason Charles Beck; 20 March 1972) is a Grammy-winning Canadian musician who resided in Paris, France for several years, and now lives in Cologne, Germany. is in Paris France
|
| 145 |
+
Chilly Gonzales (born Jason Charles Beck; 20 March 1972) is a Grammy-winning Canadian musician who resided in Paris, France for several years, and now lives in Cologne, Germany. lives in who Cologne , Germany
|
| 146 |
+
Chilly Gonzales (born Jason Charles Beck; 20 March 1972) is a Grammy-winning Canadian musician who resided in Paris, France for several years, and now lives in Cologne, Germany. is in Cologne Germany
|
| 147 |
+
Chilly Gonzales (born Jason Charles Beck; 20 March 1972) is a Grammy-winning Canadian musician who resided in Paris, France for several years, and now lives in Cologne, Germany. is Chilly Gonzales a Grammy-winning Canadian musician
|
| 148 |
+
Chilly Gonzales (born Jason Charles Beck; 20 March 1972) is a Grammy-winning Canadian musician who resided in Paris, France for several years, and now lives in Cologne, Germany. lives in who Germany
|
| 149 |
+
Known for his albums of classical piano compositions with a pop music sensibility, Solo Piano I and Solo Piano II, as well as his MC and electro albums, he is also a producer and songwriter. is he a producer
|
| 150 |
+
Known for his albums of classical piano compositions with a pop music sensibility, Solo Piano I and Solo Piano II, as well as his MC and electro albums, he is also a producer and songwriter. is he a songwriter
|
| 151 |
+
Known for his albums of classical piano compositions with a pop music sensibility, Solo Piano I and Solo Piano II, as well as his MC and electro albums, he is also a producer and songwriter. is Known for he his albums of classical piano compositions with a pop music sensibility
|
| 152 |
+
Known for his albums of classical piano compositions with a pop music sensibility, Solo Piano I and Solo Piano II, as well as his MC and electro albums, he is also a producer and songwriter. is Known for he Solo Piano I
|
| 153 |
+
Known for his albums of classical piano compositions with a pop music sensibility, Solo Piano I and Solo Piano II, as well as his MC and electro albums, he is also a producer and songwriter. is Known for he Solo Piano II
|
| 154 |
+
Known for his albums of classical piano compositions with a pop music sensibility, Solo Piano I and Solo Piano II, as well as his MC and electro albums, he is also a producer and songwriter. is Known for he his MC albums
|
| 155 |
+
Known for his albums of classical piano compositions with a pop music sensibility, Solo Piano I and Solo Piano II, as well as his MC and electro albums, he is also a producer and songwriter. is Known for he his electro albums
|
| 156 |
+
Known for his albums of classical piano compositions with a pop music sensibility, Solo Piano I and Solo Piano II, as well as his MC and electro albums, he is also a producer and songwriter. is Solo Piano I an album
|
| 157 |
+
Known for his albums of classical piano compositions with a pop music sensibility, Solo Piano I and Solo Piano II, as well as his MC and electro albums, he is also a producer and songwriter. is Solo Piano I an album of classical piano compositions
|
| 158 |
+
Known for his albums of classical piano compositions with a pop music sensibility, Solo Piano I and Solo Piano II, as well as his MC and electro albums, he is also a producer and songwriter. has Solo Piano I a pop music sensibility
|
| 159 |
+
Known for his albums of classical piano compositions with a pop music sensibility, Solo Piano I and Solo Piano II, as well as his MC and electro albums, he is also a producer and songwriter. is Solo Piano II an album
|
| 160 |
+
Known for his albums of classical piano compositions with a pop music sensibility, Solo Piano I and Solo Piano II, as well as his MC and electro albums, he is also a producer and songwriter. is Solo Piano II an album of classical piano compositions
|
| 161 |
+
Known for his albums of classical piano compositions with a pop music sensibility, Solo Piano I and Solo Piano II, as well as his MC and electro albums, he is also a producer and songwriter. has Solo Piano I a pop music sensibility
|
| 162 |
+
Gonzales broadcasts a web series Pop Music Masterclass on WDR, the documentary Classical Connections on BBC Radio 1, The History of Music on Arte, and Music's Cool with Chilly Gonzales on Apple Music's Beats1 radio show. broadcasts Gonzales Pop Music Masterclass
|
| 163 |
+
Gonzales broadcasts a web series Pop Music Masterclass on WDR, the documentary Classical Connections on BBC Radio 1, The History of Music on Arte, and Music's Cool with Chilly Gonzales on Apple Music's Beats1 radio show. is Pop Music Masterclass a web series
|
| 164 |
+
Gonzales broadcasts a web series Pop Music Masterclass on WDR, the documentary Classical Connections on BBC Radio 1, The History of Music on Arte, and Music's Cool with Chilly Gonzales on Apple Music's Beats1 radio show. broadcasts Gonzales Classical Connections
|
| 165 |
+
Gonzales broadcasts a web series Pop Music Masterclass on WDR, the documentary Classical Connections on BBC Radio 1, The History of Music on Arte, and Music's Cool with Chilly Gonzales on Apple Music's Beats1 radio show. is Classical Connections a documentary
|
| 166 |
+
Gonzales broadcasts a web series Pop Music Masterclass on WDR, the documentary Classical Connections on BBC Radio 1, The History of Music on Arte, and Music's Cool with Chilly Gonzales on Apple Music's Beats1 radio show. broadcasts Gonzales The History of Music
|
| 167 |
+
He has written several newspaper and magazine opinion pieces in The Guardian, Vice, Billboard, and others. has written He newspaper opinion pieces
|
| 168 |
+
He has written several newspaper and magazine opinion pieces in The Guardian, Vice, Billboard, and others. has written He magazine opinion pieces
|
| 169 |
+
He has written several newspaper and magazine opinion pieces in The Guardian, Vice, Billboard, and others. has written He opinion pieces
|
| 170 |
+
He is the younger brother of the prolific film composer Christophe Beck. is the younger brother of He Christophe Beck
|
| 171 |
+
He is the younger brother of the prolific film composer Christophe Beck. is Christophe Beck a film composer
|
| 172 |
+
He is the younger brother of the prolific film composer Christophe Beck. is Christophe Beck a prolific film composer
|
| 173 |
+
He is the younger brother of the prolific film composer Christophe Beck. is the brother of He Christophe Beck
|
| 174 |
+
He is the younger brother of the prolific film composer Christophe Beck. is younger than He Christophe Beck
|
| 175 |
+
Gonzales was born on 20 March 1972. was born on Gonzales 20 March 1972
|
| 176 |
+
Gonzales was born on 20 March 1972. was born in Gonzales 1972
|
| 177 |
+
His parents are Ashkenazi Jews who had to flee from Hungary during World War II. are His parents Ashkenazi Jews
|
| 178 |
+
His parents are Ashkenazi Jews who had to flee from Hungary during World War II. are His parents Jews
|
| 179 |
+
His parents are Ashkenazi Jews who had to flee from Hungary during World War II. had to flee from His parents Hungary
|
| 180 |
+
His parents are Ashkenazi Jews who had to flee from Hungary during World War II. fled from His parents Hungary
|
| 181 |
+
His parents are Ashkenazi Jews who had to flee from Hungary during World War II. has Chilly Gonzales parents
|
| 182 |
+
Gonzales began teaching himself piano at age three, when his older brother Chris began taking lessons. began teaching himself Gonzales piano
|
| 183 |
+
Gonzales began teaching himself piano at age three, when his older brother Chris began taking lessons. taught himself Gonzales piano
|
| 184 |
+
Gonzales began teaching himself piano at age three, when his older brother Chris began taking lessons. has Gonzales an older brother
|
| 185 |
+
Gonzales began teaching himself piano at age three, when his older brother Chris began taking lessons. took Chris piano lessons
|
| 186 |
+
Gonzales began teaching himself piano at age three, when his older brother Chris began taking lessons. is the older brother of Chris Gonzales
|
| 187 |
+
Gonzales graduated from Crescent School in Toronto, Ontario, Canada. graduated from Gonzales Crescent School
|
| 188 |
+
Gonzales graduated from Crescent School in Toronto, Ontario, Canada. is in Toronto Ontario , Canada
|
| 189 |
+
Gonzales graduated from Crescent School in Toronto, Ontario, Canada. is in Toronto Canada
|
| 190 |
+
Gonzales graduated from Crescent School in Toronto, Ontario, Canada. is in Ontario Canada
|
| 191 |
+
Gonzales graduated from Crescent School in Toronto, Ontario, Canada. is in Crescent School Toronto , Ontario , Canada
|
| 192 |
+
He was classically trained as a pianist at McGill University, where he began both his composing career, co-authoring several musicals with his brother, and his performing career, as a jazz virtuoso. was classically trained as He a pianist
|
| 193 |
+
He was classically trained as a pianist at McGill University, where he began both his composing career, co-authoring several musicals with his brother, and his performing career, as a jazz virtuoso. began He his composing career
|
| 194 |
+
He was classically trained as a pianist at McGill University, where he began both his composing career, co-authoring several musicals with his brother, and his performing career, as a jazz virtuoso. began He his performing career
|
| 195 |
+
He was classically trained as a pianist at McGill University, where he began both his composing career, co-authoring several musicals with his brother, and his performing career, as a jazz virtuoso. co-authored He musicals
|
| 196 |
+
He was classically trained as a pianist at McGill University, where he began both his composing career, co-authoring several musicals with his brother, and his performing career, as a jazz virtuoso. is He a jazz virtuoso
|
| 197 |
+
He was classically trained as a pianist at McGill University, where he began both his composing career, co-authoring several musicals with his brother, and his performing career, as a jazz virtuoso. has He a composing career
|
| 198 |
+
He was classically trained as a pianist at McGill University, where he began both his composing career, co-authoring several musicals with his brother, and his performing career, as a jazz virtuoso. has He a performing career
|
| 199 |
+
He was classically trained as a pianist at McGill University, where he began both his composing career, co-authoring several musicals with his brother, and his performing career, as a jazz virtuoso. has He a brother
|
| 200 |
+
In statistics, an expectation–maximization (EM) algorithm is an iterative method to find maximum likelihood or maximum a posteriori (MAP) estimates of parameters in statistical models, where the model depends on unobserved latent variables. stands for EM expectation–maximization
|
| 201 |
+
In statistics, an expectation–maximization (EM) algorithm is an iterative method to find maximum likelihood or maximum a posteriori (MAP) estimates of parameters in statistical models, where the model depends on unobserved latent variables. is an expectation–maximization algorithm an iterative method
|
| 202 |
+
In statistics, an expectation–maximization (EM) algorithm is an iterative method to find maximum likelihood or maximum a posteriori (MAP) estimates of parameters in statistical models, where the model depends on unobserved latent variables. stands for MAP maximum a posteriori
|
| 203 |
+
In statistics, an expectation–maximization (EM) algorithm is an iterative method to find maximum likelihood or maximum a posteriori (MAP) estimates of parameters in statistical models, where the model depends on unobserved latent variables. finds an expectation–maximization algorithm maximum likelihood estimates of parameters in statistical models
|
| 204 |
+
The EM iteration alternates between performing an expectation (E) step, which creates a function for the expectation of the log-likelihood evaluated using the current estimate for the parameters, and a maximization (M) step, which computes parameters maximizing the expected log-likelihood found on the E step. stands for E expectation
|
| 205 |
+
The EM iteration alternates between performing an expectation (E) step, which creates a function for the expectation of the log-likelihood evaluated using the current estimate for the parameters, and a maximization (M) step, which computes parameters maximizing the expected log-likelihood found on the E step. stands for M maximization
|
| 206 |
+
The EM iteration alternates between performing an expectation (E) step, which creates a function for the expectation of the log-likelihood evaluated using the current estimate for the parameters, and a maximization (M) step, which computes parameters maximizing the expected log-likelihood found on the E step. creates an expectation step a function for the expectation of the log-likelihood
|
| 207 |
+
The EM iteration alternates between performing an expectation (E) step, which creates a function for the expectation of the log-likelihood evaluated using the current estimate for the parameters, and a maximization (M) step, which computes parameters maximizing the expected log-likelihood found on the E step. computes a maximization step parameters
|
| 208 |
+
The EM iteration alternates between performing an expectation (E) step, which creates a function for the expectation of the log-likelihood evaluated using the current estimate for the parameters, and a maximization (M) step, which computes parameters maximizing the expected log-likelihood found on the E step. performs The EM iteration an expectation step
|
| 209 |
+
The EM iteration alternates between performing an expectation (E) step, which creates a function for the expectation of the log-likelihood evaluated using the current estimate for the parameters, and a maximization (M) step, which computes parameters maximizing the expected log-likelihood found on the E step. performs The EM iteration a maximization step
|
| 210 |
+
The EM iteration alternates between performing an expectation (E) step, which creates a function for the expectation of the log-likelihood evaluated using the current estimate for the parameters, and a maximization (M) step, which computes parameters maximizing the expected log-likelihood found on the E step. is found on the expected log-likelihood the E step
|
| 211 |
+
These parameter-estimates are then used to determine the distribution of the latent variables in the next E step. are used to determine These parameter-estimates the distribution of the latent variables
|
| 212 |
+
These parameter-estimates are then used to determine the distribution of the latent variables in the next E step. have the latent variables a distribution
|
| 213 |
+
The EM algorithm was explained and given its name in a classic 1977 paper by Arthur Dempster, Nan Laird, and Donald Rubin. was explained in The EM algorithm a classic 1977 paper by Arthur Dempster , Nan Laird , and Donald Rubin
|
| 214 |
+
The EM algorithm was explained and given its name in a classic 1977 paper by Arthur Dempster, Nan Laird, and Donald Rubin. was explained in The EM algorithm a paper
|
| 215 |
+
The EM algorithm was explained and given its name in a classic 1977 paper by Arthur Dempster, Nan Laird, and Donald Rubin. was explained in The EM algorithm 1977
|
| 216 |
+
The EM algorithm was explained and given its name in a classic 1977 paper by Arthur Dempster, Nan Laird, and Donald Rubin. explained a paper The EM algorithm
|
| 217 |
+
The EM algorithm was explained and given its name in a classic 1977 paper by Arthur Dempster, Nan Laird, and Donald Rubin. explained a classic 1977 paper by Arthur Dempster , Nan Laird , and Donald Rubin The EM algorithm
|
| 218 |
+
The EM algorithm was explained and given its name in a classic 1977 paper by Arthur Dempster, Nan Laird, and Donald Rubin. gave its name to a classic 1977 paper by Arthur Dempster , Nan Laird , and Donald Rubin The EM algorithm
|
| 219 |
+
The EM algorithm was explained and given its name in a classic 1977 paper by Arthur Dempster, Nan Laird, and Donald Rubin. gave its name to a paper The EM algorithm
|
| 220 |
+
The EM algorithm was explained and given its name in a classic 1977 paper by Arthur Dempster, Nan Laird, and Donald Rubin. wrote Arthur Dempster , Nan Laird , and Donald Rubin a classic paper
|
| 221 |
+
The EM algorithm was explained and given its name in a classic 1977 paper by Arthur Dempster, Nan Laird, and Donald Rubin. has The EM algorithm a name
|
| 222 |
+
They pointed out that the method had been "proposed many times in special circumstances" by earlier authors. had been proposed by the method earlier authors
|
| 223 |
+
They pointed out that the method had been "proposed many times in special circumstances" by earlier authors. proposed earlier authors the method
|
| 224 |
+
A very detailed treatment of the EM method for exponential families was published by Rolf Sundberg in his thesis and several papers following his collaboration with Per Martin-Löf and Anders Martin-Löf. was published by A treatment of the EM method for exponential families Rolf Sundberg
|
| 225 |
+
A very detailed treatment of the EM method for exponential families was published by Rolf Sundberg in his thesis and several papers following his collaboration with Per Martin-Löf and Anders Martin-Löf. was published by A very detailed treatment of the EM method for exponential families Rolf Sundberg
|
| 226 |
+
A very detailed treatment of the EM method for exponential families was published by Rolf Sundberg in his thesis and several papers following his collaboration with Per Martin-Löf and Anders Martin-Löf. was published by A very detailed treatment of the EM method for exponential families Rolf Sundberg
|
| 227 |
+
A very detailed treatment of the EM method for exponential families was published by Rolf Sundberg in his thesis and several papers following his collaboration with Per Martin-Löf and Anders Martin-Löf. published Rolf Sundberg A treatment of the EM method for exponential families
|
| 228 |
+
A very detailed treatment of the EM method for exponential families was published by Rolf Sundberg in his thesis and several papers following his collaboration with Per Martin-Löf and Anders Martin-Löf. published Rolf Sundberg A very detailed treatment of the EM method for exponential families
|
| 229 |
+
A very detailed treatment of the EM method for exponential families was published by Rolf Sundberg in his thesis and several papers following his collaboration with Per Martin-Löf and Anders Martin-Löf. has Rolf Sundberg a thesis
|
| 230 |
+
A very detailed treatment of the EM method for exponential families was published by Rolf Sundberg in his thesis and several papers following his collaboration with Per Martin-Löf and Anders Martin-Löf. wrote Rolf Sundberg a thesis
|
| 231 |
+
A very detailed treatment of the EM method for exponential families was published by Rolf Sundberg in his thesis and several papers following his collaboration with Per Martin-Löf and Anders Martin-Löf. collaborated with Rolf Sundberg Per Martin-Löf
|
| 232 |
+
A very detailed treatment of the EM method for exponential families was published by Rolf Sundberg in his thesis and several papers following his collaboration with Per Martin-Löf and Anders Martin-Löf. collaborated with Rolf Sundberg Anders Martin-Löf
|
| 233 |
+
A very detailed treatment of the EM method for exponential families was published by Rolf Sundberg in his thesis and several papers following his collaboration with Per Martin-Löf and Anders Martin-Löf. has a collaboration with Rolf Sundberg Per Martin-Löf
|
| 234 |
+
A very detailed treatment of the EM method for exponential families was published by Rolf Sundberg in his thesis and several papers following his collaboration with Per Martin-Löf and Anders Martin-Löf. has a collaboration with Rolf Sundberg Anders Martin-Löf
|
| 235 |
+
The Dempster–Laird–Rubin paper in 1977 generalized the method and sketched a convergence analysis for a wider class of problems. generalized The Dempster–Laird–Rubin paper the method
|
| 236 |
+
The Dempster–Laird–Rubin paper in 1977 generalized the method and sketched a convergence analysis for a wider class of problems. sketched The Dempster–Laird–Rubin paper a convergence analysis
|
| 237 |
+
The Dempster–Laird–Rubin paper in 1977 generalized the method and sketched a convergence analysis for a wider class of problems. was published in The Dempster–Laird–Rubin paper 1977
|
| 238 |
+
Regardless of earlier inventions, the innovative Dempster–Laird–Rubin paper in the Journal of the Royal Statistical Society received an enthusiastic discussion at the Royal Statistical Society meeting with Sundberg calling the paper "brilliant". received the innovative Dempster–Laird–Rubin paper in the Journal of the Royal Statistical Society an enthusiastic discussion
|
| 239 |
+
Regardless of earlier inventions, the innovative Dempster–Laird–Rubin paper in the Journal of the Royal Statistical Society received an enthusiastic discussion at the Royal Statistical Society meeting with Sundberg calling the paper "brilliant". was published in the Dempster–Laird–Rubin paper the Journal of the Royal Statistical Society
|
| 240 |
+
Regardless of earlier inventions, the innovative Dempster–Laird–Rubin paper in the Journal of the Royal Statistical Society received an enthusiastic discussion at the Royal Statistical Society meeting with Sundberg calling the paper "brilliant". called Sundberg the paper
|
| 241 |
+
Regardless of earlier inventions, the innovative Dempster–Laird–Rubin paper in the Journal of the Royal Statistical Society received an enthusiastic discussion at the Royal Statistical Society meeting with Sundberg calling the paper "brilliant". is the paper brilliant
|
| 242 |
+
Regardless of earlier inventions, the innovative Dempster–Laird–Rubin paper in the Journal of the Royal Statistical Society received an enthusiastic discussion at the Royal Statistical Society meeting with Sundberg calling the paper "brilliant". is the Dempster–Laird–Rubin paper innovative
|
| 243 |
+
Regardless of earlier inventions, the innovative Dempster–Laird–Rubin paper in the Journal of the Royal Statistical Society received an enthusiastic discussion at the Royal Statistical Society meeting with Sundberg calling the paper "brilliant". had Royal Statistical Society a meeting
|
| 244 |
+
The Dempster��Laird–Rubin paper established the EM method as an important tool of statistical analysis. established The Dempster–Laird–Rubin paper the EM method
|
| 245 |
+
The Dempster–Laird–Rubin paper established the EM method as an important tool of statistical analysis. is the EM method an important tool of statistical analysis
|
| 246 |
+
The Dempster–Laird–Rubin paper established the EM method as an important tool of statistical analysis. is the EM method a tool of statistical analysis
|
| 247 |
+
After a year of relatively healthy global economic growth, economists are predicting pretty much the same for 2018 -- a neither too-hot nor too-cold Goldilocks scenario, but with little sight of the three bears. are predicting economists the same
|
| 248 |
+
After a year of relatively healthy global economic growth, economists are predicting pretty much the same for 2018 -- a neither too-hot nor too-cold Goldilocks scenario, but with little sight of the three bears. are predicting economists a neither too-hot nor too-cold Goldilocks scenario
|
| 249 |
+
After a year of relatively healthy global economic growth, economists are predicting pretty much the same for 2018 -- a neither too-hot nor too-cold Goldilocks scenario, but with little sight of the three bears. are predicting economists a Goldilocks scenario
|
| 250 |
+
After a year of relatively healthy global economic growth, economists are predicting pretty much the same for 2018 -- a neither too-hot nor too-cold Goldilocks scenario, but with little sight of the three bears. had a a year relatively healthy global economic growth
|
| 251 |
+
After a year of relatively healthy global economic growth, economists are predicting pretty much the same for 2018 -- a neither too-hot nor too-cold Goldilocks scenario, but with little sight of the three bears. is the same a neither too-hot nor too-cold Goldilocks scenario
|
| 252 |
+
After a year of relatively healthy global economic growth, economists are predicting pretty much the same for 2018 -- a neither too-hot nor too-cold Goldilocks scenario, but with little sight of the three bears. is the same a Goldilocks scenario
|
| 253 |
+
The idea is that all is pretty much on track for growth that will be stronger than in 2017. will be stronger than growth in 2017
|
| 254 |
+
Part of this may come from the fact that forecasters generally got it wrong last year, underclubbing this year’s economic performance, particularly for the euro zone and Japan. got it wrong forecasters last year
|
| 255 |
+
Part of this may come from the fact that forecasters generally got it wrong last year, underclubbing this year’s economic performance, particularly for the euro zone and Japan. underclubbed forecasters this year economic performance
|
| 256 |
+
Part of this may come from the fact that forecasters generally got it wrong last year, underclubbing this year’s economic performance, particularly for the euro zone and Japan. has this year an economic performance
|
| 257 |
+
The International Monetary Fund, for example, saw 2017 global growth at 3.4 percent with advanced economies advancing 1.8 percent. saw The International Monetary Fund 2017 global growth
|
| 258 |
+
The International Monetary Fund, for example, saw 2017 global growth at 3.4 percent with advanced economies advancing 1.8 percent. saw The International Monetary Fund advanced economies
|
| 259 |
+
The International Monetary Fund, for example, saw 2017 global growth at 3.4 percent with advanced economies advancing 1.8 percent. was 2017 global growth 3.4 percent
|
| 260 |
+
The International Monetary Fund, for example, saw 2017 global growth at 3.4 percent with advanced economies advancing 1.8 percent. advanced advanced economies 1.8 percent
|
| 261 |
+
It now reckons them at 3.6 percent and 2.2 percent. reckons It them
|
| 262 |
+
It now reckons them at 3.6 percent and 2.2 percent. was them 3.6 percent
|
| 263 |
+
It now reckons them at 3.6 percent and 2.2 percent. was them 2.2 percent
|
| 264 |
+
It had the euro zone and Japan growing 1.5 percent and 0.6 percent, respectively. had It the euro zone
|
| 265 |
+
It had the euro zone and Japan growing 1.5 percent and 0.6 percent, respectively. had It Japan
|
| 266 |
+
It had the euro zone and Japan growing 1.5 percent and 0.6 percent, respectively. predicted the growth of It the euro zone
|
| 267 |
+
It had the euro zone and Japan growing 1.5 percent and 0.6 percent, respectively. predicted the growth of It Japan
|
| 268 |
+
It had the euro zone and Japan growing 1.5 percent and 0.6 percent, respectively. grew the euro zone 1.5 percent
|
| 269 |
+
It had the euro zone and Japan growing 1.5 percent and 0.6 percent, respectively. grew Japan 0.6 percent
|
| 270 |
+
It had the euro zone and Japan growing 1.5 percent and 0.6 percent, respectively. has the euro a zone
|
| 271 |
+
It now has them at 2.1 percent and 1.5 percent. has It them
|
| 272 |
+
“Faster growth is reaching roughly two-thirds of the world’s population,” the IMF said in a December blog post. is reaching Faster growth two-thirds of the world ’ s population
|
| 273 |
+
“Faster growth is reaching roughly two-thirds of the world’s population,” the IMF said in a December blog post. said the IMF “ Faster growth is reaching roughly two-thirds of the world ’ s population , ”
|
| 274 |
+
“Faster growth is reaching roughly two-thirds of the world’s population,” the IMF said in a December blog post. has the IMF a blog
|
| 275 |
+
This performance has made some economists optimistic. has made This performance some economists
|
| 276 |
+
Nomura is among the more bullish: “Global growth has far more self-reinforcing characteristics at present than at any time over the last 20-30 years.” is among Nomura the more bullish
|
| 277 |
+
Nomura is among the more bullish: “Global growth has far more self-reinforcing characteristics at present than at any time over the last 20-30 years.” is Nomura bullish
|
| 278 |
+
Nomura is among the more bullish: “Global growth has far more self-reinforcing characteristics at present than at any time over the last 20-30 years.” has more Global growth self-reinforcing characteristics at present
|
| 279 |
+
Nomura is among the more bullish: “Global growth has far more self-reinforcing characteristics at present than at any time over the last 20-30 years.” has Global growth self-reinforcing characteristics
|
| 280 |
+
There are huge numbers of potential political and economic risks to the status quo. are risks to the status quo political and economic
|
| 281 |
+
There are huge numbers of potential political and economic risks to the status quo. threaten economic risks the status quo
|
| 282 |
+
There are huge numbers of potential political and economic risks to the status quo. threaten potential political risks the status quo
|
| 283 |
+
There are huge numbers of potential political and economic risks to the status quo. threaten political risks the status quo
|
| 284 |
+
But as in the fairy tale, let’s go with just three: central banks, trade, and bubbles. are a risk to central banks a year of relatively healthy global economic growth
|
| 285 |
+
But as in the fairy tale, let’s go with just three: central banks, trade, and bubbles. is a risk to trade a year of relatively healthy global economic growth
|
| 286 |
+
But as in the fairy tale, let’s go with just three: central banks, trade, and bubbles. is a risk to bubbles a year of relatively healthy global economic growth
|
| 287 |
+
In the first case, the danger is that there will be a policy mistake, squeezing debtors. would squeeze a policy mistake debtors
|
| 288 |
+
In the first case, the danger is that there will be a policy mistake, squeezing debtors. is a policy mistake a danger
|
| 289 |
+
The second relates to renewed U.S. protectionism or anger over Chinese exports triggering tit-for-tat, growth-stifling trade barriers. could trigger renewed U.S. protectionism trade barriers
|
| 290 |
+
The second relates to renewed U.S. protectionism or anger over Chinese exports triggering tit-for-tat, growth-stifling trade barriers. could trigger anger over Chinese exports trade barriers
|
| 291 |
+
The second relates to renewed U.S. protectionism or anger over Chinese exports triggering tit-for-tat, growth-stifling trade barriers. stifle trade barriers growth
|
| 292 |
+
The third is about sudden market losses that dry up spending and demand. is about The third sudden market losses
|
| 293 |
+
The third is about sudden market losses that dry up spending and demand. could dry up sudden market losses spending
|
| 294 |
+
The third is about sudden market losses that dry up spending and demand. could dry up sudden market losses demand
|
evaluation_data/wire57/gold_data/gold_wire_57_test.txt
ADDED
|
@@ -0,0 +1,294 @@
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|
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|
| 1 |
+
Tokyo (ˈtoʊkioʊ, Japanese: toːkʲoː), officially Tokyo Metropolis, is the capital city of Japan and one of its 47 prefectures. <arg1> Tokyo </arg1> <rel> is in Japanese </rel> <arg2> toːkʲoː </arg2> 1
|
| 2 |
+
Tokyo (ˈtoʊkioʊ, Japanese: toːkʲoː), officially Tokyo Metropolis, is the capital city of Japan and one of its 47 prefectures. <arg1> Tokyo </arg1> <rel> is pronounced </rel> <arg2> ˈtoʊkioʊ </arg2> 1
|
| 3 |
+
Tokyo (ˈtoʊkioʊ, Japanese: toːkʲoː), officially Tokyo Metropolis, is the capital city of Japan and one of its 47 prefectures. <arg1> Tokyo </arg1> <rel> is officially </rel> <arg2> Tokyo Metropolis </arg2> 1
|
| 4 |
+
Tokyo (ˈtoʊkioʊ, Japanese: toːkʲoː), officially Tokyo Metropolis, is the capital city of Japan and one of its 47 prefectures. <arg1> Tokyo </arg1> <rel> is the capital city of </rel> <arg2> Japan </arg2> 1
|
| 5 |
+
Tokyo (ˈtoʊkioʊ, Japanese: toːkʲoː), officially Tokyo Metropolis, is the capital city of Japan and one of its 47 prefectures. <arg1> Tokyo </arg1> <rel> is </rel> <arg2> the capital city of Japan </arg2> 1
|
| 6 |
+
Tokyo (ˈtoʊkioʊ, Japanese: toːkʲoː), officially Tokyo Metropolis, is the capital city of Japan and one of its 47 prefectures. <arg1> Tokyo </arg1> <rel> is one of </rel> <arg2> its prefectures </arg2> 1
|
| 7 |
+
Tokyo (ˈtoʊkioʊ, Japanese: toːkʲoː), officially Tokyo Metropolis, is the capital city of Japan and one of its 47 prefectures. <arg1> Tokyo </arg1> <rel> is one of </rel> <arg2> its 47 prefectures </arg2> 1
|
| 8 |
+
Tokyo (ˈtoʊkioʊ, Japanese: toːkʲoː), officially Tokyo Metropolis, is the capital city of Japan and one of its 47 prefectures. <arg1> Tokyo </arg1> <rel> is a prefecture of </rel> <arg2> Japan </arg2> 1
|
| 9 |
+
Tokyo (ˈtoʊkioʊ, Japanese: toːkʲoː), officially Tokyo Metropolis, is the capital city of Japan and one of its 47 prefectures. <arg1> Japan </arg1> <rel> has </rel> <arg2> 47 prefectures </arg2> 1
|
| 10 |
+
Tokyo (ˈtoʊkioʊ, Japanese: toːkʲoː), officially Tokyo Metropolis, is the capital city of Japan and one of its 47 prefectures. <arg1> Tokyo </arg1> <rel> is </rel> <arg2> a prefecture </arg2> 1
|
| 11 |
+
Tokyo (ˈtoʊkioʊ, Japanese: toːkʲoː), officially Tokyo Metropolis, is the capital city of Japan and one of its 47 prefectures. <arg1> Tokyo </arg1> <rel> is </rel> <arg2> a city </arg2> 1
|
| 12 |
+
The Greater Tokyo Area is the most populous metropolitan area in the world. <arg1> The Greater Tokyo Area </arg1> <rel> is </rel> <arg2> the most populous metropolitan area </arg2> 1
|
| 13 |
+
The Greater Tokyo Area is the most populous metropolitan area in the world. <arg1> The Greater Tokyo Area </arg1> <rel> is </rel> <arg2> a metropolitan area </arg2> 1
|
| 14 |
+
It is the seat of the Emperor of Japan and the Japanese government. <arg1> It </arg1> <rel> is the seat of </rel> <arg2> the Emperor of Japan </arg2> 1
|
| 15 |
+
It is the seat of the Emperor of Japan and the Japanese government. <arg1> It </arg1> <rel> is the seat of </rel> <arg2> the Japanese government </arg2> 1
|
| 16 |
+
It is the seat of the Emperor of Japan and the Japanese government. <arg1> Japan </arg1> <rel> has </rel> <arg2> an Emperor </arg2> 1
|
| 17 |
+
It is the seat of the Emperor of Japan and the Japanese government. <arg1> Japan </arg1> <rel> has </rel> <arg2> a government </arg2> 1
|
| 18 |
+
Tokyo is in the Kantō region on the southeastern side of the main island Honshu and includes the Izu Islands and Ogasawara Islands. <arg1> Tokyo </arg1> <rel> is in </rel> <arg2> the Kantō region </arg2> 1
|
| 19 |
+
Tokyo is in the Kantō region on the southeastern side of the main island Honshu and includes the Izu Islands and Ogasawara Islands. <arg1> Tokyo </arg1> <rel> is in </rel> <arg2> Kantō </arg2> 1
|
| 20 |
+
Tokyo is in the Kantō region on the southeastern side of the main island Honshu and includes the Izu Islands and Ogasawara Islands. <arg1> Kantō </arg1> <rel> is </rel> <arg2> a region </arg2> 1
|
| 21 |
+
Tokyo is in the Kantō region on the southeastern side of the main island Honshu and includes the Izu Islands and Ogasawara Islands. <arg1> Tokyo </arg1> <rel> is on </rel> <arg2> the southeastern side of the main island Honshu </arg2> 1
|
| 22 |
+
Tokyo is in the Kantō region on the southeastern side of the main island Honshu and includes the Izu Islands and Ogasawara Islands. <arg1> the Kantō region </arg1> <rel> is on </rel> <arg2> the southeastern side of the main island Honshu </arg2> 1
|
| 23 |
+
Tokyo is in the Kantō region on the southeastern side of the main island Honshu and includes the Izu Islands and Ogasawara Islands. <arg1> Honshu </arg1> <rel> is </rel> <arg2> an island </arg2> 1
|
| 24 |
+
Tokyo is in the Kantō region on the southeastern side of the main island Honshu and includes the Izu Islands and Ogasawara Islands. <arg1> Honshu </arg1> <rel> is </rel> <arg2> the main island </arg2> 1
|
| 25 |
+
Tokyo is in the Kantō region on the southeastern side of the main island Honshu and includes the Izu Islands and Ogasawara Islands. <arg1> Tokyo </arg1> <rel> includes </rel> <arg2> the Izu Islands </arg2> 1
|
| 26 |
+
Tokyo is in the Kantō region on the southeastern side of the main island Honshu and includes the Izu Islands and Ogasawara Islands. <arg1> Tokyo </arg1> <rel> includes </rel> <arg2> the Ogasawara Islands </arg2> 1
|
| 27 |
+
Tokyo is in the Kantō region on the southeastern side of the main island Honshu and includes the Izu Islands and Ogasawara Islands. <arg1> Tokyo </arg1> <rel> has </rel> <arg2> islands </arg2> 1
|
| 28 |
+
Tokyo is in the Kantō region on the southeastern side of the main island Honshu and includes the Izu Islands and Ogasawara Islands. <arg1> Honshu </arg1> <rel> has </rel> <arg2> a southeastern side </arg2> 1
|
| 29 |
+
Formerly known as Edo, it has been the de facto seat of government since 1603 when Shogun Tokugawa Ieyasu made the city his headquarters. <arg1> it </arg1> <rel> was Formerly known as </rel> <arg2> Edo </arg2> 1
|
| 30 |
+
Formerly known as Edo, it has been the de facto seat of government since 1603 when Shogun Tokugawa Ieyasu made the city his headquarters. <arg1> it </arg1> <rel> has been </rel> <arg2> the seat of government </arg2> 1
|
| 31 |
+
Formerly known as Edo, it has been the de facto seat of government since 1603 when Shogun Tokugawa Ieyasu made the city his headquarters. <arg1> Tokugawa Ieyasu </arg1> <rel> made </rel> <arg2> the city </arg2> 1
|
| 32 |
+
Formerly known as Edo, it has been the de facto seat of government since 1603 when Shogun Tokugawa Ieyasu made the city his headquarters. <arg1> Tokugawa Ieyasu </arg1> <rel> is </rel> <arg2> Shogun </arg2> 1
|
| 33 |
+
Formerly known as Edo, it has been the de facto seat of government since 1603 when Shogun Tokugawa Ieyasu made the city his headquarters. <arg1> it </arg1> <rel> is </rel> <arg2> the seat of government </arg2> 1
|
| 34 |
+
Formerly known as Edo, it has been the de facto seat of government since 1603 when Shogun Tokugawa Ieyasu made the city his headquarters. <arg1> Tokugawa Ieyasu </arg1> <rel> has </rel> <arg2> headquarters </arg2> 1
|
| 35 |
+
It officially became the capital after Emperor Meiji moved his seat to the city from the old capital of Kyoto in 1868; at that time Edo was renamed Tokyo. <arg1> It </arg1> <rel> became </rel> <arg2> the capital </arg2> 1
|
| 36 |
+
It officially became the capital after Emperor Meiji moved his seat to the city from the old capital of Kyoto in 1868; at that time Edo was renamed Tokyo. <arg1> Emperor Meiji </arg1> <rel> moved </rel> <arg2> his seat </arg2> 1
|
| 37 |
+
It officially became the capital after Emperor Meiji moved his seat to the city from the old capital of Kyoto in 1868; at that time Edo was renamed Tokyo. <arg1> Edo </arg1> <rel> was renamed </rel> <arg2> Tokyo </arg2> 1
|
| 38 |
+
It officially became the capital after Emperor Meiji moved his seat to the city from the old capital of Kyoto in 1868; at that time Edo was renamed Tokyo. <arg1> Kyoto </arg1> <rel> is </rel> <arg2> the old capital </arg2> 1
|
| 39 |
+
Tokyo Metropolis was formed in 1943 from the merger of the former Tokyo Prefecture (東京府 Tōkyō-fu) and the city of Tokyo (東京市 Tōkyō-shi). <arg1> Tokyo Metropolis </arg1> <rel> was formed in </rel> <arg2> 1943 </arg2> 1
|
| 40 |
+
Tokyo Metropolis was formed in 1943 from the merger of the former Tokyo Prefecture (東京府 Tōkyō-fu) and the city of Tokyo (東京市 Tōkyō-shi). <arg1> Tokyo Metropolis </arg1> <rel> was formed from </rel> <arg2> the merger of the Tokyo Prefecture and the city of Tokyo </arg2> 1
|
| 41 |
+
Tokyo Metropolis was formed in 1943 from the merger of the former Tokyo Prefecture (東京府 Tōkyō-fu) and the city of Tokyo (東京市 Tōkyō-shi). <arg1> the city of Tokyo </arg1> <rel> is </rel> <arg2> Tōkyō-shi </arg2> 1
|
| 42 |
+
Tokyo Metropolis was formed in 1943 from the merger of the former Tokyo Prefecture (東京府 Tōkyō-fu) and the city of Tokyo (東京市 Tōkyō-shi). <arg1> the city of Tokyo </arg1> <rel> is </rel> <arg2> 東京市 </arg2> 1
|
| 43 |
+
Tokyo Metropolis was formed in 1943 from the merger of the former Tokyo Prefecture (東京府 Tōkyō-fu) and the city of Tokyo (東京市 Tōkyō-shi). <arg1> the former Tokyo Prefecture </arg1> <rel> is </rel> <arg2> 東京府 </arg2> 1
|
| 44 |
+
Tokyo Metropolis was formed in 1943 from the merger of the former Tokyo Prefecture (東京府 Tōkyō-fu) and the city of Tokyo (東京市 Tōkyō-shi). <arg1> the former Tokyo Prefecture </arg1> <rel> is </rel> <arg2> Tōkyō-fu </arg2> 1
|
| 45 |
+
Tokyo Metropolis was formed in 1943 from the merger of the former Tokyo Prefecture (東京府 Tōkyō-fu) and the city of Tokyo (東京市 Tōkyō-shi). <arg1> the former Tokyo Prefecture </arg1> <rel> merged with </rel> <arg2> the city of Tokyo </arg2> 1
|
| 46 |
+
Tokyo is often referred to as a city, but is officially known and governed as a "metropolitan prefecture", which differs from and combines elements of a city and a prefecture, a characteristic unique to Tokyo. <arg1> Tokyo </arg1> <rel> is referred to as </rel> <arg2> a city </arg2> 1
|
| 47 |
+
Tokyo is often referred to as a city, but is officially known and governed as a "metropolitan prefecture", which differs from and combines elements of a city and a prefecture, a characteristic unique to Tokyo. <arg1> Tokyo </arg1> <rel> is officially known as </rel> <arg2> a `` metropolitan prefecture '' </arg2> 1
|
| 48 |
+
Tokyo is often referred to as a city, but is officially known and governed as a "metropolitan prefecture", which differs from and combines elements of a city and a prefecture, a characteristic unique to Tokyo. <arg1> Tokyo </arg1> <rel> is governed as </rel> <arg2> a `` metropolitan prefecture '' </arg2> 1
|
| 49 |
+
Tokyo is often referred to as a city, but is officially known and governed as a "metropolitan prefecture", which differs from and combines elements of a city and a prefecture, a characteristic unique to Tokyo. <arg1> a `` metropolitan prefecture '' </arg1> <rel> differs from </rel> <arg2> a city </arg2> 1
|
| 50 |
+
Tokyo is often referred to as a city, but is officially known and governed as a "metropolitan prefecture", which differs from and combines elements of a city and a prefecture, a characteristic unique to Tokyo. <arg1> a `` metropolitan prefecture '' </arg1> <rel> differs from </rel> <arg2> a prefecture </arg2> 1
|
| 51 |
+
Tokyo is often referred to as a city, but is officially known and governed as a "metropolitan prefecture", which differs from and combines elements of a city and a prefecture, a characteristic unique to Tokyo. <arg1> a `` metropolitan prefecture '' </arg1> <rel> combines elements of </rel> <arg2> a city and a prefecture </arg2> 1
|
| 52 |
+
Tokyo is often referred to as a city, but is officially known and governed as a "metropolitan prefecture", which differs from and combines elements of a city and a prefecture, a characteristic unique to Tokyo. <arg1> a characteristic </arg1> <rel> is unique to </rel> <arg2> Tokyo </arg2> 1
|
| 53 |
+
The Tokyo metropolitan government administers the 23 Special Wards of Tokyo (each governed as an individual city), which cover the area that was the City of Tokyo before it merged and became the metropolitan prefecture in 1943. <arg1> The Tokyo metropolitan government </arg1> <rel> administers </rel> <arg2> the 23 Special Wards of Tokyo </arg2> 1
|
| 54 |
+
The Tokyo metropolitan government administers the 23 Special Wards of Tokyo (each governed as an individual city), which cover the area that was the City of Tokyo before it merged and became the metropolitan prefecture in 1943. <arg1> Tokyo </arg1> <rel> has </rel> <arg2> 23 Special Wards </arg2> 1
|
| 55 |
+
The Tokyo metropolitan government administers the 23 Special Wards of Tokyo (each governed as an individual city), which cover the area that was the City of Tokyo before it merged and became the metropolitan prefecture in 1943. <arg1> each </arg1> <rel> is governed as </rel> <arg2> an individual city </arg2> 1
|
| 56 |
+
The Tokyo metropolitan government administers the 23 Special Wards of Tokyo (each governed as an individual city), which cover the area that was the City of Tokyo before it merged and became the metropolitan prefecture in 1943. <arg1> the City of Tokyo </arg1> <rel> merged </rel> <arg2> in 1943 </arg2> 1
|
| 57 |
+
The Tokyo metropolitan government administers the 23 Special Wards of Tokyo (each governed as an individual city), which cover the area that was the City of Tokyo before it merged and became the metropolitan prefecture in 1943. <arg1> the City of Tokyo </arg1> <rel> became </rel> <arg2> the metropolitan prefecture </arg2> 1
|
| 58 |
+
The Tokyo metropolitan government administers the 23 Special Wards of Tokyo (each governed as an individual city), which cover the area that was the City of Tokyo before it merged and became the metropolitan prefecture in 1943. <arg1> Tokyo </arg1> <rel> has </rel> <arg2> a metropolitan government </arg2> 1
|
| 59 |
+
The Tokyo metropolitan government administers the 23 Special Wards of Tokyo (each governed as an individual city), which cover the area that was the City of Tokyo before it merged and became the metropolitan prefecture in 1943. <arg1> the City of Tokyo </arg1> <rel> is </rel> <arg2> an area </arg2> 1
|
| 60 |
+
The metropolitan government also administers 39 municipalities in the western part of the prefecture and the two outlying island chains. <arg1> The metropolitan government </arg1> <rel> administers </rel> <arg2> 39 municipalities </arg2> 1
|
| 61 |
+
The metropolitan government also administers 39 municipalities in the western part of the prefecture and the two outlying island chains. <arg1> The metropolitan government </arg1> <rel> administers </rel> <arg2> the two outlying island chains </arg2> 1
|
| 62 |
+
The metropolitan government also administers 39 municipalities in the western part of the prefecture and the two outlying island chains. <arg1> the prefecture </arg1> <rel> has </rel> <arg2> a western part </arg2> 1
|
| 63 |
+
The metropolitan government also administers 39 municipalities in the western part of the prefecture and the two outlying island chains. <arg1> the two island chains </arg1> <rel> are </rel> <arg2> outliers </arg2> 1
|
| 64 |
+
The population of the special wards is over 9 million people, with the total population of the prefecture exceeding 13 million. <arg1> The population of the special wards </arg1> <rel> is over </rel> <arg2> 9 million people </arg2> 1
|
| 65 |
+
The population of the special wards is over 9 million people, with the total population of the prefecture exceeding 13 million. <arg1> the total population of the prefecture </arg1> <rel> exceeds </rel> <arg2> 13 million </arg2> 1
|
| 66 |
+
The population of the special wards is over 9 million people, with the total population of the prefecture exceeding 13 million. <arg1> the prefecture </arg1> <rel> has </rel> <arg2> a population </arg2> 1
|
| 67 |
+
The population of the special wards is over 9 million people, with the total population of the prefecture exceeding 13 million. <arg1> the special wards </arg1> <rel> have </rel> <arg2> a population </arg2> 1
|
| 68 |
+
The prefecture is part of the world's most populous metropolitan area with upwards of 37.8 million people and the world's largest urban agglomeration economy. <arg1> The prefecture </arg1> <rel> is part of </rel> <arg2> the world 's most populous metropolitan area </arg2> 1
|
| 69 |
+
The prefecture is part of the world's most populous metropolitan area with upwards of 37.8 million people and the world's largest urban agglomeration economy. <arg1> the world 's most populous metropolitan area </arg1> <rel> has upwards of </rel> <arg2> 37.8 million people </arg2> 1
|
| 70 |
+
The prefecture is part of the world's most populous metropolitan area with upwards of 37.8 million people and the world's largest urban agglomeration economy. <arg1> The prefecture </arg1> <rel> is part of </rel> <arg2> the world 's largest urban agglomeration economy </arg2> 1
|
| 71 |
+
The prefecture is part of the world's most populous metropolitan area with upwards of 37.8 million people and the world's largest urban agglomeration economy. <arg1> the world </arg1> <rel> has </rel> <arg2> metropolitan areas </arg2> 1
|
| 72 |
+
The prefecture is part of the world's most populous metropolitan area with upwards of 37.8 million people and the world's largest urban agglomeration economy. <arg1> the world </arg1> <rel> has </rel> <arg2> urban agglomeration economies </arg2> 1
|
| 73 |
+
The prefecture is part of the world's most populous metropolitan area with upwards of 37.8 million people and the world's largest urban agglomeration economy. <arg1> the world </arg1> <rel> has </rel> <arg2> a most populous metropolitan area </arg2> 1
|
| 74 |
+
The prefecture is part of the world's most populous metropolitan area with upwards of 37.8 million people and the world's largest urban agglomeration economy. <arg1> the world </arg1> <rel> has </rel> <arg2> a largest urban agglomeration economy </arg2> 1
|
| 75 |
+
The prefecture is part of the world's most populous metropolitan area with upwards of 37.8 million people and the world's largest urban agglomeration economy. <arg1> urban agglomerations </arg1> <rel> have </rel> <arg2> economies </arg2> 1
|
| 76 |
+
In 2011, the city hosted 51 of the Fortune Global 500 companies, the highest number of any city in the world, at that time. <arg1> the city </arg1> <rel> hosted </rel> <arg2> 51 of the Fortune Global 500 companies </arg2> 1
|
| 77 |
+
In 2011, the city hosted 51 of the Fortune Global 500 companies, the highest number of any city in the world, at that time. <arg1> the city </arg1> <rel> hosted </rel> <arg2> the highest number of any city in the world </arg2> 1
|
| 78 |
+
In 2011, the city hosted 51 of the Fortune Global 500 companies, the highest number of any city in the world, at that time. <arg1> the city </arg1> <rel> hosted </rel> <arg2> companies </arg2> 1
|
| 79 |
+
In 2011, the city hosted 51 of the Fortune Global 500 companies, the highest number of any city in the world, at that time. <arg1> any city </arg1> <rel> has </rel> <arg2> a number of Fortune Global 500 companies </arg2> 1
|
| 80 |
+
Tokyo ranked third (twice) in the International Financial Centres Development IndexEdit. <arg1> Tokyo </arg1> <rel> ranked </rel> <arg2> third </arg2> 1
|
| 81 |
+
The city is also home to various television networks such as Fuji TV, Tokyo MX, TV Tokyo, TV Asahi, Nippon Television, NHK and the Tokyo Broadcasting System. <arg1> The city </arg1> <rel> is home to </rel> <arg2> various television networks </arg2> 1
|
| 82 |
+
The city is also home to various television networks such as Fuji TV, Tokyo MX, TV Tokyo, TV Asahi, Nippon Television, NHK and the Tokyo Broadcasting System. <arg1> The city </arg1> <rel> is home to </rel> <arg2> Fuji TV </arg2> 1
|
| 83 |
+
The city is also home to various television networks such as Fuji TV, Tokyo MX, TV Tokyo, TV Asahi, Nippon Television, NHK and the Tokyo Broadcasting System. <arg1> Fuji TV </arg1> <rel> is </rel> <arg2> a television network </arg2> 1
|
| 84 |
+
The city is also home to various television networks such as Fuji TV, Tokyo MX, TV Tokyo, TV Asahi, Nippon Television, NHK and the Tokyo Broadcasting System. <arg1> The city </arg1> <rel> is home to </rel> <arg2> Tokyo MX </arg2> 1
|
| 85 |
+
The city is also home to various television networks such as Fuji TV, Tokyo MX, TV Tokyo, TV Asahi, Nippon Television, NHK and the Tokyo Broadcasting System. <arg1> Tokyo MX </arg1> <rel> is </rel> <arg2> a television network </arg2> 1
|
| 86 |
+
The city is also home to various television networks such as Fuji TV, Tokyo MX, TV Tokyo, TV Asahi, Nippon Television, NHK and the Tokyo Broadcasting System. <arg1> The city </arg1> <rel> is home to </rel> <arg2> TV Tokyo </arg2> 1
|
| 87 |
+
The city is also home to various television networks such as Fuji TV, Tokyo MX, TV Tokyo, TV Asahi, Nippon Television, NHK and the Tokyo Broadcasting System. <arg1> TV Tokyo </arg1> <rel> is </rel> <arg2> a television network </arg2> 1
|
| 88 |
+
The city is also home to various television networks such as Fuji TV, Tokyo MX, TV Tokyo, TV Asahi, Nippon Television, NHK and the Tokyo Broadcasting System. <arg1> The city </arg1> <rel> is home to </rel> <arg2> TV Asahi </arg2> 1
|
| 89 |
+
The city is also home to various television networks such as Fuji TV, Tokyo MX, TV Tokyo, TV Asahi, Nippon Television, NHK and the Tokyo Broadcasting System. <arg1> TV Asahi </arg1> <rel> is </rel> <arg2> a television network </arg2> 1
|
| 90 |
+
The city is also home to various television networks such as Fuji TV, Tokyo MX, TV Tokyo, TV Asahi, Nippon Television, NHK and the Tokyo Broadcasting System. <arg1> The city </arg1> <rel> is home to </rel> <arg2> Nippon Television </arg2> 1
|
| 91 |
+
The city is also home to various television networks such as Fuji TV, Tokyo MX, TV Tokyo, TV Asahi, Nippon Television, NHK and the Tokyo Broadcasting System. <arg1> Nippon Television </arg1> <rel> is </rel> <arg2> a television network </arg2> 1
|
| 92 |
+
The city is also home to various television networks such as Fuji TV, Tokyo MX, TV Tokyo, TV Asahi, Nippon Television, NHK and the Tokyo Broadcasting System. <arg1> The city </arg1> <rel> is home to </rel> <arg2> NHK </arg2> 1
|
| 93 |
+
The city is also home to various television networks such as Fuji TV, Tokyo MX, TV Tokyo, TV Asahi, Nippon Television, NHK and the Tokyo Broadcasting System. <arg1> NHK </arg1> <rel> is </rel> <arg2> a television network </arg2> 1
|
| 94 |
+
The city is also home to various television networks such as Fuji TV, Tokyo MX, TV Tokyo, TV Asahi, Nippon Television, NHK and the Tokyo Broadcasting System. <arg1> The city </arg1> <rel> is home to </rel> <arg2> the Tokyo Broadcasting System </arg2> 1
|
| 95 |
+
The city is also home to various television networks such as Fuji TV, Tokyo MX, TV Tokyo, TV Asahi, Nippon Television, NHK and the Tokyo Broadcasting System. <arg1> the Tokyo Broadcasting System </arg1> <rel> is </rel> <arg2> a television network </arg2> 1
|
| 96 |
+
Finnish police reprimanded a man for traveling in a car boot to hide his meeting with Prime Minister Juha Sipila during a government crisis last summer, saying this was breach of the traffic code. <arg1> Finnish police </arg1> <rel> reprimanded </rel> <arg2> a man </arg2> 1
|
| 97 |
+
Finnish police reprimanded a man for traveling in a car boot to hide his meeting with Prime Minister Juha Sipila during a government crisis last summer, saying this was breach of the traffic code. <arg1> a man </arg1> <rel> met with </rel> <arg2> Juha Sipila </arg2> 1
|
| 98 |
+
Finnish police reprimanded a man for traveling in a car boot to hide his meeting with Prime Minister Juha Sipila during a government crisis last summer, saying this was breach of the traffic code. <arg1> Juha Sipila </arg1> <rel> is </rel> <arg2> Prime Minister </arg2> 1
|
| 99 |
+
Finnish police reprimanded a man for traveling in a car boot to hide his meeting with Prime Minister Juha Sipila during a government crisis last summer, saying this was breach of the traffic code. <arg1> this </arg1> <rel> was </rel> <arg2> a breach of the traffic code </arg2> 1
|
| 100 |
+
Finnish police reprimanded a man for traveling in a car boot to hide his meeting with Prime Minister Juha Sipila during a government crisis last summer, saying this was breach of the traffic code. <arg1> a government crisis </arg1> <rel> happened </rel> <arg2> last summer </arg2> 1
|
| 101 |
+
Finnish police reprimanded a man for traveling in a car boot to hide his meeting with Prime Minister Juha Sipila during a government crisis last summer, saying this was breach of the traffic code. <arg1> a man </arg1> <rel> had </rel> <arg2> a meeting with Juha Sipila </arg2> 1
|
| 102 |
+
Finnish police reprimanded a man for traveling in a car boot to hide his meeting with Prime Minister Juha Sipila during a government crisis last summer, saying this was breach of the traffic code. <arg1> the government </arg1> <rel> had </rel> <arg2> a crisis </arg2> 1
|
| 103 |
+
A police statement did not name the man in the boot, but in effect indicated the traveler was State Secretary Samuli Virtanen, who is also the deputy to Foreign Minister Timo Soini. <arg1> A police statement </arg1> <rel> did not name </rel> <arg2> the man in the boot </arg2> 1
|
| 104 |
+
A police statement did not name the man in the boot, but in effect indicated the traveler was State Secretary Samuli Virtanen, who is also the deputy to Foreign Minister Timo Soini. <arg1> the man in the boot </arg1> <rel> was </rel> <arg2> Samuli Virtanen </arg2> 1
|
| 105 |
+
A police statement did not name the man in the boot, but in effect indicated the traveler was State Secretary Samuli Virtanen, who is also the deputy to Foreign Minister Timo Soini. <arg1> the traveler </arg1> <rel> was </rel> <arg2> Samuli Virtanen </arg2> 1
|
| 106 |
+
A police statement did not name the man in the boot, but in effect indicated the traveler was State Secretary Samuli Virtanen, who is also the deputy to Foreign Minister Timo Soini. <arg1> Samuli Virtanen </arg1> <rel> is </rel> <arg2> State Secretary </arg2> 1
|
| 107 |
+
A police statement did not name the man in the boot, but in effect indicated the traveler was State Secretary Samuli Virtanen, who is also the deputy to Foreign Minister Timo Soini. <arg1> Samuli Virtanen </arg1> <rel> is </rel> <arg2> the deputy to Foreign Minister Timo Soini </arg2> 1
|
| 108 |
+
A police statement did not name the man in the boot, but in effect indicated the traveler was State Secretary Samuli Virtanen, who is also the deputy to Foreign Minister Timo Soini. <arg1> Samuli Virtanen </arg1> <rel> is </rel> <arg2> a deputy </arg2> 1
|
| 109 |
+
A police statement did not name the man in the boot, but in effect indicated the traveler was State Secretary Samuli Virtanen, who is also the deputy to Foreign Minister Timo Soini. <arg1> Timo Soini </arg1> <rel> is </rel> <arg2> Foreign Minister </arg2> 1
|
| 110 |
+
A police statement did not name the man in the boot, but in effect indicated the traveler was State Secretary Samuli Virtanen, who is also the deputy to Foreign Minister Timo Soini. <arg1> Timo Soini </arg1> <rel> has </rel> <arg2> a deputy </arg2> 1
|
| 111 |
+
The meeting took place in June, a day after Virtanen’s co-ruling Finns party had elected anti-immigration hardliners as its new leaders. <arg1> The meeting </arg1> <rel> took place in </rel> <arg2> June </arg2> 1
|
| 112 |
+
The meeting took place in June, a day after Virtanen’s co-ruling Finns party had elected anti-immigration hardliners as its new leaders. <arg1> the Finns party </arg1> <rel> elected </rel> <arg2> anti-immigration hardliners </arg2> 1
|
| 113 |
+
The meeting took place in June, a day after Virtanen’s co-ruling Finns party had elected anti-immigration hardliners as its new leaders. <arg1> the Finns party </arg1> <rel> is </rel> <arg2> co-ruling </arg2> 1
|
| 114 |
+
The meeting took place in June, a day after Virtanen’s co-ruling Finns party had elected anti-immigration hardliners as its new leaders. <arg1> Virtanen </arg1> <rel> belongs to </rel> <arg2> the Finns party </arg2> 1
|
| 115 |
+
The meeting took place in June, a day after Virtanen’s co-ruling Finns party had elected anti-immigration hardliners as its new leaders. <arg1> the Finns party </arg1> <rel> has </rel> <arg2> new leaders </arg2> 1
|
| 116 |
+
The meeting took place in June, a day after Virtanen’s co-ruling Finns party had elected anti-immigration hardliners as its new leaders. <arg1> anti-immigration hardliners </arg1> <rel> are </rel> <arg2> its new leaders </arg2> 1
|
| 117 |
+
The meeting took place in June, a day after Virtanen’s co-ruling Finns party had elected anti-immigration hardliners as its new leaders. <arg1> the Finns party </arg1> <rel> has </rel> <arg2> leaders </arg2> 1
|
| 118 |
+
The government was close to collapse until a group of politicians, including Virtanen and Soini, in the following week walked out of the Finns party and announced they would form a new group. <arg1> The government </arg1> <rel> was close to </rel> <arg2> collapse </arg2> 1
|
| 119 |
+
The government was close to collapse until a group of politicians, including Virtanen and Soini, in the following week walked out of the Finns party and announced they would form a new group. <arg1> a group of politicians </arg1> <rel> walked out of </rel> <arg2> the Finns party </arg2> 1
|
| 120 |
+
The government was close to collapse until a group of politicians, including Virtanen and Soini, in the following week walked out of the Finns party and announced they would form a new group. <arg1> Virtanen </arg1> <rel> walked out of </rel> <arg2> the Finns party </arg2> 1
|
| 121 |
+
The government was close to collapse until a group of politicians, including Virtanen and Soini, in the following week walked out of the Finns party and announced they would form a new group. <arg1> Soini </arg1> <rel> walked out of </rel> <arg2> the Finns party </arg2> 1
|
| 122 |
+
The government was close to collapse until a group of politicians, including Virtanen and Soini, in the following week walked out of the Finns party and announced they would form a new group. <arg1> Virtanen </arg1> <rel> is </rel> <arg2> a politician </arg2> 1
|
| 123 |
+
The government was close to collapse until a group of politicians, including Virtanen and Soini, in the following week walked out of the Finns party and announced they would form a new group. <arg1> Soini </arg1> <rel> is </rel> <arg2> a politician </arg2> 1
|
| 124 |
+
The government was close to collapse until a group of politicians, including Virtanen and Soini, in the following week walked out of the Finns party and announced they would form a new group. <arg1> a group of politicians </arg1> <rel> would form </rel> <arg2> a new group </arg2> 1
|
| 125 |
+
The government was close to collapse until a group of politicians, including Virtanen and Soini, in the following week walked out of the Finns party and announced they would form a new group. <arg1> a group of politicians including Virtanen and Soini </arg1> <rel> would form </rel> <arg2> a new group </arg2> 1
|
| 126 |
+
The Finns party was thrown out of the government and the new “Blue Reform” group kept its cabinet seat. <arg1> The Finns party </arg1> <rel> was thrown out of </rel> <arg2> the government </arg2> 1
|
| 127 |
+
The Finns party was thrown out of the government and the new “Blue Reform” group kept its cabinet seat. <arg1> Blue Reform </arg1> <rel> kept </rel> <arg2> its cabinet seat </arg2> 1
|
| 128 |
+
The Finns party was thrown out of the government and the new “Blue Reform” group kept its cabinet seat. <arg1> the new group </arg1> <rel> kept </rel> <arg2> its cabinet seat </arg2> 1
|
| 129 |
+
The Finns party was thrown out of the government and the new “Blue Reform” group kept its cabinet seat. <arg1> Blue Reform </arg1> <rel> is </rel> <arg2> a group </arg2> 1
|
| 130 |
+
Virtanen has not commented on the case, but lawmaker Tiina Elovaara from Blue Reform said in a blog that Virtanen climbed into the boot to keep the meeting secret at a critical moment. <arg1> Virtanen </arg1> <rel> has not commented on </rel> <arg2> the case </arg2> 1
|
| 131 |
+
Virtanen has not commented on the case, but lawmaker Tiina Elovaara from Blue Reform said in a blog that Virtanen climbed into the boot to keep the meeting secret at a critical moment. <arg1> Tiina Elovaara </arg1> <rel> is </rel> <arg2> a lawmaker </arg2> 1
|
| 132 |
+
Virtanen has not commented on the case, but lawmaker Tiina Elovaara from Blue Reform said in a blog that Virtanen climbed into the boot to keep the meeting secret at a critical moment. <arg1> Tiina Elovaara </arg1> <rel> is from </rel> <arg2> Blue Reform </arg2> 1
|
| 133 |
+
Virtanen has not commented on the case, but lawmaker Tiina Elovaara from Blue Reform said in a blog that Virtanen climbed into the boot to keep the meeting secret at a critical moment. <arg1> Virtanen </arg1> <rel> climbed into </rel> <arg2> the boot </arg2> 1
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“He avoided media attention when the situation was most serious, and the risk of leakage about the parliamentarians’ transition was too big,” Elovaara said. <arg1> He </arg1> <rel> avoided </rel> <arg2> media attention </arg2> 1
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“He avoided media attention when the situation was most serious, and the risk of leakage about the parliamentarians’ transition was too big,” Elovaara said. <arg1> the risk of leakage about the parliamentarians ’ transition </arg1> <rel> was </rel> <arg2> too big </arg2> 1
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Chilly Gonzales (born Jason Charles Beck; 20 March 1972) is a Grammy-winning Canadian musician who resided in Paris, France for several years, and now lives in Cologne, Germany. <arg1> Chilly Gonzales </arg1> <rel> was born </rel> <arg2> Jason Charles Beck </arg2> 1
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Chilly Gonzales (born Jason Charles Beck; 20 March 1972) is a Grammy-winning Canadian musician who resided in Paris, France for several years, and now lives in Cologne, Germany. <arg1> Chilly Gonzales </arg1> <rel> was born on </rel> <arg2> 20 March 1972 </arg2> 1
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Chilly Gonzales (born Jason Charles Beck; 20 March 1972) is a Grammy-winning Canadian musician who resided in Paris, France for several years, and now lives in Cologne, Germany. <arg1> Chilly Gonzales </arg1> <rel> was born in </rel> <arg2> 1972 </arg2> 1
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Chilly Gonzales (born Jason Charles Beck; 20 March 1972) is a Grammy-winning Canadian musician who resided in Paris, France for several years, and now lives in Cologne, Germany. <arg1> Chilly Gonzales </arg1> <rel> is </rel> <arg2> Canadian </arg2> 1
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Chilly Gonzales (born Jason Charles Beck; 20 March 1972) is a Grammy-winning Canadian musician who resided in Paris, France for several years, and now lives in Cologne, Germany. <arg1> Chilly Gonzales </arg1> <rel> won </rel> <arg2> a Grammy </arg2> 1
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Chilly Gonzales (born Jason Charles Beck; 20 March 1972) is a Grammy-winning Canadian musician who resided in Paris, France for several years, and now lives in Cologne, Germany. <arg1> Chilly Gonzales </arg1> <rel> is </rel> <arg2> a musician </arg2> 1
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Chilly Gonzales (born Jason Charles Beck; 20 March 1972) is a Grammy-winning Canadian musician who resided in Paris, France for several years, and now lives in Cologne, Germany. <arg1> who </arg1> <rel> resided in </rel> <arg2> Paris , France </arg2> 1
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Chilly Gonzales (born Jason Charles Beck; 20 March 1972) is a Grammy-winning Canadian musician who resided in Paris, France for several years, and now lives in Cologne, Germany. <arg1> who </arg1> <rel> resided in </rel> <arg2> France </arg2> 1
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Chilly Gonzales (born Jason Charles Beck; 20 March 1972) is a Grammy-winning Canadian musician who resided in Paris, France for several years, and now lives in Cologne, Germany. <arg1> Paris </arg1> <rel> is in </rel> <arg2> France </arg2> 1
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Chilly Gonzales (born Jason Charles Beck; 20 March 1972) is a Grammy-winning Canadian musician who resided in Paris, France for several years, and now lives in Cologne, Germany. <arg1> who </arg1> <rel> lives in </rel> <arg2> Cologne , Germany </arg2> 1
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Chilly Gonzales (born Jason Charles Beck; 20 March 1972) is a Grammy-winning Canadian musician who resided in Paris, France for several years, and now lives in Cologne, Germany. <arg1> Cologne </arg1> <rel> is in </rel> <arg2> Germany </arg2> 1
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Chilly Gonzales (born Jason Charles Beck; 20 March 1972) is a Grammy-winning Canadian musician who resided in Paris, France for several years, and now lives in Cologne, Germany. <arg1> Chilly Gonzales </arg1> <rel> is </rel> <arg2> a Grammy-winning Canadian musician </arg2> 1
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Chilly Gonzales (born Jason Charles Beck; 20 March 1972) is a Grammy-winning Canadian musician who resided in Paris, France for several years, and now lives in Cologne, Germany. <arg1> who </arg1> <rel> lives in </rel> <arg2> Germany </arg2> 1
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Known for his albums of classical piano compositions with a pop music sensibility, Solo Piano I and Solo Piano II, as well as his MC and electro albums, he is also a producer and songwriter. <arg1> he </arg1> <rel> is </rel> <arg2> a producer </arg2> 1
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Known for his albums of classical piano compositions with a pop music sensibility, Solo Piano I and Solo Piano II, as well as his MC and electro albums, he is also a producer and songwriter. <arg1> he </arg1> <rel> is </rel> <arg2> a songwriter </arg2> 1
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Known for his albums of classical piano compositions with a pop music sensibility, Solo Piano I and Solo Piano II, as well as his MC and electro albums, he is also a producer and songwriter. <arg1> he </arg1> <rel> is Known for </rel> <arg2> his albums of classical piano compositions with a pop music sensibility </arg2> 1
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Known for his albums of classical piano compositions with a pop music sensibility, Solo Piano I and Solo Piano II, as well as his MC and electro albums, he is also a producer and songwriter. <arg1> he </arg1> <rel> is Known for </rel> <arg2> Solo Piano I </arg2> 1
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Known for his albums of classical piano compositions with a pop music sensibility, Solo Piano I and Solo Piano II, as well as his MC and electro albums, he is also a producer and songwriter. <arg1> he </arg1> <rel> is Known for </rel> <arg2> Solo Piano II </arg2> 1
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Known for his albums of classical piano compositions with a pop music sensibility, Solo Piano I and Solo Piano II, as well as his MC and electro albums, he is also a producer and songwriter. <arg1> he </arg1> <rel> is Known for </rel> <arg2> his MC albums </arg2> 1
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Known for his albums of classical piano compositions with a pop music sensibility, Solo Piano I and Solo Piano II, as well as his MC and electro albums, he is also a producer and songwriter. <arg1> he </arg1> <rel> is Known for </rel> <arg2> his electro albums </arg2> 1
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Known for his albums of classical piano compositions with a pop music sensibility, Solo Piano I and Solo Piano II, as well as his MC and electro albums, he is also a producer and songwriter. <arg1> Solo Piano I </arg1> <rel> is </rel> <arg2> an album </arg2> 1
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Known for his albums of classical piano compositions with a pop music sensibility, Solo Piano I and Solo Piano II, as well as his MC and electro albums, he is also a producer and songwriter. <arg1> Solo Piano I </arg1> <rel> is </rel> <arg2> an album of classical piano compositions </arg2> 1
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Known for his albums of classical piano compositions with a pop music sensibility, Solo Piano I and Solo Piano II, as well as his MC and electro albums, he is also a producer and songwriter. <arg1> Solo Piano I </arg1> <rel> has </rel> <arg2> a pop music sensibility </arg2> 1
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Known for his albums of classical piano compositions with a pop music sensibility, Solo Piano I and Solo Piano II, as well as his MC and electro albums, he is also a producer and songwriter. <arg1> Solo Piano II </arg1> <rel> is </rel> <arg2> an album </arg2> 1
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Known for his albums of classical piano compositions with a pop music sensibility, Solo Piano I and Solo Piano II, as well as his MC and electro albums, he is also a producer and songwriter. <arg1> Solo Piano II </arg1> <rel> is </rel> <arg2> an album of classical piano compositions </arg2> 1
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Known for his albums of classical piano compositions with a pop music sensibility, Solo Piano I and Solo Piano II, as well as his MC and electro albums, he is also a producer and songwriter. <arg1> Solo Piano I </arg1> <rel> has </rel> <arg2> a pop music sensibility </arg2> 1
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Gonzales broadcasts a web series Pop Music Masterclass on WDR, the documentary Classical Connections on BBC Radio 1, The History of Music on Arte, and Music's Cool with Chilly Gonzales on Apple Music's Beats1 radio show. <arg1> Gonzales </arg1> <rel> broadcasts </rel> <arg2> Pop Music Masterclass </arg2> 1
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Gonzales broadcasts a web series Pop Music Masterclass on WDR, the documentary Classical Connections on BBC Radio 1, The History of Music on Arte, and Music's Cool with Chilly Gonzales on Apple Music's Beats1 radio show. <arg1> Pop Music Masterclass </arg1> <rel> is </rel> <arg2> a web series </arg2> 1
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Gonzales broadcasts a web series Pop Music Masterclass on WDR, the documentary Classical Connections on BBC Radio 1, The History of Music on Arte, and Music's Cool with Chilly Gonzales on Apple Music's Beats1 radio show. <arg1> Gonzales </arg1> <rel> broadcasts </rel> <arg2> Classical Connections </arg2> 1
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Gonzales broadcasts a web series Pop Music Masterclass on WDR, the documentary Classical Connections on BBC Radio 1, The History of Music on Arte, and Music's Cool with Chilly Gonzales on Apple Music's Beats1 radio show. <arg1> Classical Connections </arg1> <rel> is </rel> <arg2> a documentary </arg2> 1
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Gonzales broadcasts a web series Pop Music Masterclass on WDR, the documentary Classical Connections on BBC Radio 1, The History of Music on Arte, and Music's Cool with Chilly Gonzales on Apple Music's Beats1 radio show. <arg1> Gonzales </arg1> <rel> broadcasts </rel> <arg2> The History of Music </arg2> 1
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He has written several newspaper and magazine opinion pieces in The Guardian, Vice, Billboard, and others. <arg1> He </arg1> <rel> has written </rel> <arg2> newspaper opinion pieces </arg2> 1
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He has written several newspaper and magazine opinion pieces in The Guardian, Vice, Billboard, and others. <arg1> He </arg1> <rel> has written </rel> <arg2> magazine opinion pieces </arg2> 1
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He has written several newspaper and magazine opinion pieces in The Guardian, Vice, Billboard, and others. <arg1> He </arg1> <rel> has written </rel> <arg2> opinion pieces </arg2> 1
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He is the younger brother of the prolific film composer Christophe Beck. <arg1> He </arg1> <rel> is the younger brother of </rel> <arg2> Christophe Beck </arg2> 1
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He is the younger brother of the prolific film composer Christophe Beck. <arg1> Christophe Beck </arg1> <rel> is </rel> <arg2> a film composer </arg2> 1
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He is the younger brother of the prolific film composer Christophe Beck. <arg1> Christophe Beck </arg1> <rel> is </rel> <arg2> a prolific film composer </arg2> 1
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He is the younger brother of the prolific film composer Christophe Beck. <arg1> He </arg1> <rel> is the brother of </rel> <arg2> Christophe Beck </arg2> 1
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He is the younger brother of the prolific film composer Christophe Beck. <arg1> He </arg1> <rel> is younger than </rel> <arg2> Christophe Beck </arg2> 1
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Gonzales was born on 20 March 1972. <arg1> Gonzales </arg1> <rel> was born on </rel> <arg2> 20 March 1972 </arg2> 1
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Gonzales was born on 20 March 1972. <arg1> Gonzales </arg1> <rel> was born in </rel> <arg2> 1972 </arg2> 1
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His parents are Ashkenazi Jews who had to flee from Hungary during World War II. <arg1> His parents </arg1> <rel> are </rel> <arg2> Ashkenazi Jews </arg2> 1
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His parents are Ashkenazi Jews who had to flee from Hungary during World War II. <arg1> His parents </arg1> <rel> are </rel> <arg2> Jews </arg2> 1
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His parents are Ashkenazi Jews who had to flee from Hungary during World War II. <arg1> His parents </arg1> <rel> had to flee from </rel> <arg2> Hungary </arg2> 1
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His parents are Ashkenazi Jews who had to flee from Hungary during World War II. <arg1> His parents </arg1> <rel> fled from </rel> <arg2> Hungary </arg2> 1
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His parents are Ashkenazi Jews who had to flee from Hungary during World War II. <arg1> Chilly Gonzales </arg1> <rel> has </rel> <arg2> parents </arg2> 1
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Gonzales began teaching himself piano at age three, when his older brother Chris began taking lessons. <arg1> Gonzales </arg1> <rel> began teaching himself </rel> <arg2> piano </arg2> 1
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Gonzales began teaching himself piano at age three, when his older brother Chris began taking lessons. <arg1> Gonzales </arg1> <rel> taught himself </rel> <arg2> piano </arg2> 1
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Gonzales began teaching himself piano at age three, when his older brother Chris began taking lessons. <arg1> Gonzales </arg1> <rel> has </rel> <arg2> an older brother </arg2> 1
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Gonzales began teaching himself piano at age three, when his older brother Chris began taking lessons. <arg1> Chris </arg1> <rel> took </rel> <arg2> piano lessons </arg2> 1
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Gonzales began teaching himself piano at age three, when his older brother Chris began taking lessons. <arg1> Chris </arg1> <rel> is the older brother of </rel> <arg2> Gonzales </arg2> 1
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Gonzales graduated from Crescent School in Toronto, Ontario, Canada. <arg1> Gonzales </arg1> <rel> graduated from </rel> <arg2> Crescent School </arg2> 1
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Gonzales graduated from Crescent School in Toronto, Ontario, Canada. <arg1> Toronto </arg1> <rel> is in </rel> <arg2> Ontario , Canada </arg2> 1
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Gonzales graduated from Crescent School in Toronto, Ontario, Canada. <arg1> Toronto </arg1> <rel> is in </rel> <arg2> Canada </arg2> 1
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Gonzales graduated from Crescent School in Toronto, Ontario, Canada. <arg1> Ontario </arg1> <rel> is in </rel> <arg2> Canada </arg2> 1
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Gonzales graduated from Crescent School in Toronto, Ontario, Canada. <arg1> Crescent School </arg1> <rel> is in </rel> <arg2> Toronto , Ontario , Canada </arg2> 1
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He was classically trained as a pianist at McGill University, where he began both his composing career, co-authoring several musicals with his brother, and his performing career, as a jazz virtuoso. <arg1> He </arg1> <rel> was classically trained as </rel> <arg2> a pianist </arg2> 1
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He was classically trained as a pianist at McGill University, where he began both his composing career, co-authoring several musicals with his brother, and his performing career, as a jazz virtuoso. <arg1> He </arg1> <rel> began </rel> <arg2> his composing career </arg2> 1
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He was classically trained as a pianist at McGill University, where he began both his composing career, co-authoring several musicals with his brother, and his performing career, as a jazz virtuoso. <arg1> He </arg1> <rel> began </rel> <arg2> his performing career </arg2> 1
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| 195 |
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He was classically trained as a pianist at McGill University, where he began both his composing career, co-authoring several musicals with his brother, and his performing career, as a jazz virtuoso. <arg1> He </arg1> <rel> co-authored </rel> <arg2> musicals </arg2> 1
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| 196 |
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He was classically trained as a pianist at McGill University, where he began both his composing career, co-authoring several musicals with his brother, and his performing career, as a jazz virtuoso. <arg1> He </arg1> <rel> is </rel> <arg2> a jazz virtuoso </arg2> 1
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| 197 |
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He was classically trained as a pianist at McGill University, where he began both his composing career, co-authoring several musicals with his brother, and his performing career, as a jazz virtuoso. <arg1> He </arg1> <rel> has </rel> <arg2> a composing career </arg2> 1
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He was classically trained as a pianist at McGill University, where he began both his composing career, co-authoring several musicals with his brother, and his performing career, as a jazz virtuoso. <arg1> He </arg1> <rel> has </rel> <arg2> a performing career </arg2> 1
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He was classically trained as a pianist at McGill University, where he began both his composing career, co-authoring several musicals with his brother, and his performing career, as a jazz virtuoso. <arg1> He </arg1> <rel> has </rel> <arg2> a brother </arg2> 1
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In statistics, an expectation–maximization (EM) algorithm is an iterative method to find maximum likelihood or maximum a posteriori (MAP) estimates of parameters in statistical models, where the model depends on unobserved latent variables. <arg1> EM </arg1> <rel> stands for </rel> <arg2> expectation–maximization </arg2> 1
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In statistics, an expectation–maximization (EM) algorithm is an iterative method to find maximum likelihood or maximum a posteriori (MAP) estimates of parameters in statistical models, where the model depends on unobserved latent variables. <arg1> an expectation–maximization algorithm </arg1> <rel> is </rel> <arg2> an iterative method </arg2> 1
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In statistics, an expectation–maximization (EM) algorithm is an iterative method to find maximum likelihood or maximum a posteriori (MAP) estimates of parameters in statistical models, where the model depends on unobserved latent variables. <arg1> MAP </arg1> <rel> stands for </rel> <arg2> maximum a posteriori </arg2> 1
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| 203 |
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In statistics, an expectation–maximization (EM) algorithm is an iterative method to find maximum likelihood or maximum a posteriori (MAP) estimates of parameters in statistical models, where the model depends on unobserved latent variables. <arg1> an expectation–maximization algorithm </arg1> <rel> finds </rel> <arg2> maximum likelihood estimates of parameters in statistical models </arg2> 1
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The EM iteration alternates between performing an expectation (E) step, which creates a function for the expectation of the log-likelihood evaluated using the current estimate for the parameters, and a maximization (M) step, which computes parameters maximizing the expected log-likelihood found on the E step. <arg1> E </arg1> <rel> stands for </rel> <arg2> expectation </arg2> 1
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| 205 |
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The EM iteration alternates between performing an expectation (E) step, which creates a function for the expectation of the log-likelihood evaluated using the current estimate for the parameters, and a maximization (M) step, which computes parameters maximizing the expected log-likelihood found on the E step. <arg1> M </arg1> <rel> stands for </rel> <arg2> maximization </arg2> 1
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| 206 |
+
The EM iteration alternates between performing an expectation (E) step, which creates a function for the expectation of the log-likelihood evaluated using the current estimate for the parameters, and a maximization (M) step, which computes parameters maximizing the expected log-likelihood found on the E step. <arg1> an expectation step </arg1> <rel> creates </rel> <arg2> a function for the expectation of the log-likelihood </arg2> 1
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| 207 |
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The EM iteration alternates between performing an expectation (E) step, which creates a function for the expectation of the log-likelihood evaluated using the current estimate for the parameters, and a maximization (M) step, which computes parameters maximizing the expected log-likelihood found on the E step. <arg1> a maximization step </arg1> <rel> computes </rel> <arg2> parameters </arg2> 1
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| 208 |
+
The EM iteration alternates between performing an expectation (E) step, which creates a function for the expectation of the log-likelihood evaluated using the current estimate for the parameters, and a maximization (M) step, which computes parameters maximizing the expected log-likelihood found on the E step. <arg1> The EM iteration </arg1> <rel> performs </rel> <arg2> an expectation step </arg2> 1
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| 209 |
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The EM iteration alternates between performing an expectation (E) step, which creates a function for the expectation of the log-likelihood evaluated using the current estimate for the parameters, and a maximization (M) step, which computes parameters maximizing the expected log-likelihood found on the E step. <arg1> The EM iteration </arg1> <rel> performs </rel> <arg2> a maximization step </arg2> 1
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| 210 |
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The EM iteration alternates between performing an expectation (E) step, which creates a function for the expectation of the log-likelihood evaluated using the current estimate for the parameters, and a maximization (M) step, which computes parameters maximizing the expected log-likelihood found on the E step. <arg1> the expected log-likelihood </arg1> <rel> is found on </rel> <arg2> the E step </arg2> 1
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These parameter-estimates are then used to determine the distribution of the latent variables in the next E step. <arg1> These parameter-estimates </arg1> <rel> are used to determine </rel> <arg2> the distribution of the latent variables </arg2> 1
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These parameter-estimates are then used to determine the distribution of the latent variables in the next E step. <arg1> the latent variables </arg1> <rel> have </rel> <arg2> a distribution </arg2> 1
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The EM algorithm was explained and given its name in a classic 1977 paper by Arthur Dempster, Nan Laird, and Donald Rubin. <arg1> The EM algorithm </arg1> <rel> was explained in </rel> <arg2> a classic 1977 paper by Arthur Dempster , Nan Laird , and Donald Rubin </arg2> 1
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| 214 |
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The EM algorithm was explained and given its name in a classic 1977 paper by Arthur Dempster, Nan Laird, and Donald Rubin. <arg1> The EM algorithm </arg1> <rel> was explained in </rel> <arg2> a paper </arg2> 1
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| 215 |
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The EM algorithm was explained and given its name in a classic 1977 paper by Arthur Dempster, Nan Laird, and Donald Rubin. <arg1> The EM algorithm </arg1> <rel> was explained in </rel> <arg2> 1977 </arg2> 1
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| 216 |
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The EM algorithm was explained and given its name in a classic 1977 paper by Arthur Dempster, Nan Laird, and Donald Rubin. <arg1> a paper </arg1> <rel> explained </rel> <arg2> The EM algorithm </arg2> 1
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| 217 |
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The EM algorithm was explained and given its name in a classic 1977 paper by Arthur Dempster, Nan Laird, and Donald Rubin. <arg1> a classic 1977 paper by Arthur Dempster , Nan Laird , and Donald Rubin </arg1> <rel> explained </rel> <arg2> The EM algorithm </arg2> 1
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The EM algorithm was explained and given its name in a classic 1977 paper by Arthur Dempster, Nan Laird, and Donald Rubin. <arg1> a classic 1977 paper by Arthur Dempster , Nan Laird , and Donald Rubin </arg1> <rel> gave its name to </rel> <arg2> The EM algorithm </arg2> 1
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| 219 |
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The EM algorithm was explained and given its name in a classic 1977 paper by Arthur Dempster, Nan Laird, and Donald Rubin. <arg1> a paper </arg1> <rel> gave its name to </rel> <arg2> The EM algorithm </arg2> 1
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| 220 |
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The EM algorithm was explained and given its name in a classic 1977 paper by Arthur Dempster, Nan Laird, and Donald Rubin. <arg1> Arthur Dempster , Nan Laird , and Donald Rubin </arg1> <rel> wrote </rel> <arg2> a classic paper </arg2> 1
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| 221 |
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The EM algorithm was explained and given its name in a classic 1977 paper by Arthur Dempster, Nan Laird, and Donald Rubin. <arg1> The EM algorithm </arg1> <rel> has </rel> <arg2> a name </arg2> 1
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They pointed out that the method had been "proposed many times in special circumstances" by earlier authors. <arg1> the method </arg1> <rel> had been proposed by </rel> <arg2> earlier authors </arg2> 1
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They pointed out that the method had been "proposed many times in special circumstances" by earlier authors. <arg1> earlier authors </arg1> <rel> proposed </rel> <arg2> the method </arg2> 1
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A very detailed treatment of the EM method for exponential families was published by Rolf Sundberg in his thesis and several papers following his collaboration with Per Martin-Löf and Anders Martin-Löf. <arg1> A treatment of the EM method for exponential families </arg1> <rel> was published by </rel> <arg2> Rolf Sundberg </arg2> 1
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| 225 |
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A very detailed treatment of the EM method for exponential families was published by Rolf Sundberg in his thesis and several papers following his collaboration with Per Martin-Löf and Anders Martin-Löf. <arg1> A very detailed treatment of the EM method for exponential families </arg1> <rel> was published by </rel> <arg2> Rolf Sundberg </arg2> 1
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| 226 |
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A very detailed treatment of the EM method for exponential families was published by Rolf Sundberg in his thesis and several papers following his collaboration with Per Martin-Löf and Anders Martin-Löf. <arg1> A very detailed treatment of the EM method for exponential families </arg1> <rel> was published by </rel> <arg2> Rolf Sundberg </arg2> 1
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| 227 |
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A very detailed treatment of the EM method for exponential families was published by Rolf Sundberg in his thesis and several papers following his collaboration with Per Martin-Löf and Anders Martin-Löf. <arg1> Rolf Sundberg </arg1> <rel> published </rel> <arg2> A treatment of the EM method for exponential families </arg2> 1
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| 228 |
+
A very detailed treatment of the EM method for exponential families was published by Rolf Sundberg in his thesis and several papers following his collaboration with Per Martin-Löf and Anders Martin-Löf. <arg1> Rolf Sundberg </arg1> <rel> published </rel> <arg2> A very detailed treatment of the EM method for exponential families </arg2> 1
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| 229 |
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A very detailed treatment of the EM method for exponential families was published by Rolf Sundberg in his thesis and several papers following his collaboration with Per Martin-Löf and Anders Martin-Löf. <arg1> Rolf Sundberg </arg1> <rel> has </rel> <arg2> a thesis </arg2> 1
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| 230 |
+
A very detailed treatment of the EM method for exponential families was published by Rolf Sundberg in his thesis and several papers following his collaboration with Per Martin-Löf and Anders Martin-Löf. <arg1> Rolf Sundberg </arg1> <rel> wrote </rel> <arg2> a thesis </arg2> 1
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| 231 |
+
A very detailed treatment of the EM method for exponential families was published by Rolf Sundberg in his thesis and several papers following his collaboration with Per Martin-Löf and Anders Martin-Löf. <arg1> Rolf Sundberg </arg1> <rel> collaborated with </rel> <arg2> Per Martin-Löf </arg2> 1
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| 232 |
+
A very detailed treatment of the EM method for exponential families was published by Rolf Sundberg in his thesis and several papers following his collaboration with Per Martin-Löf and Anders Martin-Löf. <arg1> Rolf Sundberg </arg1> <rel> collaborated with </rel> <arg2> Anders Martin-Löf </arg2> 1
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| 233 |
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A very detailed treatment of the EM method for exponential families was published by Rolf Sundberg in his thesis and several papers following his collaboration with Per Martin-Löf and Anders Martin-Löf. <arg1> Rolf Sundberg </arg1> <rel> has a collaboration with </rel> <arg2> Per Martin-Löf </arg2> 1
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| 234 |
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A very detailed treatment of the EM method for exponential families was published by Rolf Sundberg in his thesis and several papers following his collaboration with Per Martin-Löf and Anders Martin-Löf. <arg1> Rolf Sundberg </arg1> <rel> has a collaboration with </rel> <arg2> Anders Martin-Löf </arg2> 1
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| 235 |
+
The Dempster–Laird–Rubin paper in 1977 generalized the method and sketched a convergence analysis for a wider class of problems. <arg1> The Dempster–Laird–Rubin paper </arg1> <rel> generalized </rel> <arg2> the method </arg2> 1
|
| 236 |
+
The Dempster–Laird–Rubin paper in 1977 generalized the method and sketched a convergence analysis for a wider class of problems. <arg1> The Dempster–Laird–Rubin paper </arg1> <rel> sketched </rel> <arg2> a convergence analysis </arg2> 1
|
| 237 |
+
The Dempster–Laird–Rubin paper in 1977 generalized the method and sketched a convergence analysis for a wider class of problems. <arg1> The Dempster–Laird–Rubin paper </arg1> <rel> was published in </rel> <arg2> 1977 </arg2> 1
|
| 238 |
+
Regardless of earlier inventions, the innovative Dempster–Laird–Rubin paper in the Journal of the Royal Statistical Society received an enthusiastic discussion at the Royal Statistical Society meeting with Sundberg calling the paper "brilliant". <arg1> the innovative Dempster–Laird–Rubin paper in the Journal of the Royal Statistical Society </arg1> <rel> received </rel> <arg2> an enthusiastic discussion </arg2> 1
|
| 239 |
+
Regardless of earlier inventions, the innovative Dempster–Laird–Rubin paper in the Journal of the Royal Statistical Society received an enthusiastic discussion at the Royal Statistical Society meeting with Sundberg calling the paper "brilliant". <arg1> the Dempster–Laird–Rubin paper </arg1> <rel> was published in </rel> <arg2> the Journal of the Royal Statistical Society </arg2> 1
|
| 240 |
+
Regardless of earlier inventions, the innovative Dempster–Laird–Rubin paper in the Journal of the Royal Statistical Society received an enthusiastic discussion at the Royal Statistical Society meeting with Sundberg calling the paper "brilliant". <arg1> Sundberg </arg1> <rel> called </rel> <arg2> the paper </arg2> 1
|
| 241 |
+
Regardless of earlier inventions, the innovative Dempster–Laird–Rubin paper in the Journal of the Royal Statistical Society received an enthusiastic discussion at the Royal Statistical Society meeting with Sundberg calling the paper "brilliant". <arg1> the paper </arg1> <rel> is </rel> <arg2> brilliant </arg2> 1
|
| 242 |
+
Regardless of earlier inventions, the innovative Dempster–Laird–Rubin paper in the Journal of the Royal Statistical Society received an enthusiastic discussion at the Royal Statistical Society meeting with Sundberg calling the paper "brilliant". <arg1> the Dempster–Laird–Rubin paper </arg1> <rel> is </rel> <arg2> innovative </arg2> 1
|
| 243 |
+
Regardless of earlier inventions, the innovative Dempster–Laird–Rubin paper in the Journal of the Royal Statistical Society received an enthusiastic discussion at the Royal Statistical Society meeting with Sundberg calling the paper "brilliant". <arg1> Royal Statistical Society </arg1> <rel> had </rel> <arg2> a meeting </arg2> 1
|
| 244 |
+
The Dempster–Laird–Rubin paper established the EM method as an important tool of statistical analysis. <arg1> The Dempster–Laird–Rubin paper </arg1> <rel> established </rel> <arg2> the EM method </arg2> 1
|
| 245 |
+
The Dempster–Laird–Rubin paper established the EM method as an important tool of statistical analysis. <arg1> the EM method </arg1> <rel> is </rel> <arg2> an important tool of statistical analysis </arg2> 1
|
| 246 |
+
The Dempster–Laird–Rubin paper established the EM method as an important tool of statistical analysis. <arg1> the EM method </arg1> <rel> is </rel> <arg2> a tool of statistical analysis </arg2> 1
|
| 247 |
+
After a year of relatively healthy global economic growth, economists are predicting pretty much the same for 2018 -- a neither too-hot nor too-cold Goldilocks scenario, but with little sight of the three bears. <arg1> economists </arg1> <rel> are predicting </rel> <arg2> the same </arg2> 1
|
| 248 |
+
After a year of relatively healthy global economic growth, economists are predicting pretty much the same for 2018 -- a neither too-hot nor too-cold Goldilocks scenario, but with little sight of the three bears. <arg1> economists </arg1> <rel> are predicting </rel> <arg2> a neither too-hot nor too-cold Goldilocks scenario </arg2> 1
|
| 249 |
+
After a year of relatively healthy global economic growth, economists are predicting pretty much the same for 2018 -- a neither too-hot nor too-cold Goldilocks scenario, but with little sight of the three bears. <arg1> economists </arg1> <rel> are predicting </rel> <arg2> a Goldilocks scenario </arg2> 1
|
| 250 |
+
After a year of relatively healthy global economic growth, economists are predicting pretty much the same for 2018 -- a neither too-hot nor too-cold Goldilocks scenario, but with little sight of the three bears. <arg1> a year </arg1> <rel> had a </rel> <arg2> relatively healthy global economic growth </arg2> 1
|
| 251 |
+
After a year of relatively healthy global economic growth, economists are predicting pretty much the same for 2018 -- a neither too-hot nor too-cold Goldilocks scenario, but with little sight of the three bears. <arg1> the same </arg1> <rel> is </rel> <arg2> a neither too-hot nor too-cold Goldilocks scenario </arg2> 1
|
| 252 |
+
After a year of relatively healthy global economic growth, economists are predicting pretty much the same for 2018 -- a neither too-hot nor too-cold Goldilocks scenario, but with little sight of the three bears. <arg1> the same </arg1> <rel> is </rel> <arg2> a Goldilocks scenario </arg2> 1
|
| 253 |
+
The idea is that all is pretty much on track for growth that will be stronger than in 2017. <arg1> growth </arg1> <rel> will be stronger than </rel> <arg2> in 2017 </arg2> 1
|
| 254 |
+
Part of this may come from the fact that forecasters generally got it wrong last year, underclubbing this year’s economic performance, particularly for the euro zone and Japan. <arg1> forecasters </arg1> <rel> got it wrong </rel> <arg2> last year </arg2> 1
|
| 255 |
+
Part of this may come from the fact that forecasters generally got it wrong last year, underclubbing this year’s economic performance, particularly for the euro zone and Japan. <arg1> forecasters </arg1> <rel> underclubbed </rel> <arg2> this year economic performance </arg2> 1
|
| 256 |
+
Part of this may come from the fact that forecasters generally got it wrong last year, underclubbing this year’s economic performance, particularly for the euro zone and Japan. <arg1> this year </arg1> <rel> has </rel> <arg2> an economic performance </arg2> 1
|
| 257 |
+
The International Monetary Fund, for example, saw 2017 global growth at 3.4 percent with advanced economies advancing 1.8 percent. <arg1> The International Monetary Fund </arg1> <rel> saw </rel> <arg2> 2017 global growth </arg2> 1
|
| 258 |
+
The International Monetary Fund, for example, saw 2017 global growth at 3.4 percent with advanced economies advancing 1.8 percent. <arg1> The International Monetary Fund </arg1> <rel> saw </rel> <arg2> advanced economies </arg2> 1
|
| 259 |
+
The International Monetary Fund, for example, saw 2017 global growth at 3.4 percent with advanced economies advancing 1.8 percent. <arg1> 2017 global growth </arg1> <rel> was </rel> <arg2> 3.4 percent </arg2> 1
|
| 260 |
+
The International Monetary Fund, for example, saw 2017 global growth at 3.4 percent with advanced economies advancing 1.8 percent. <arg1> advanced economies </arg1> <rel> advanced </rel> <arg2> 1.8 percent </arg2> 1
|
| 261 |
+
It now reckons them at 3.6 percent and 2.2 percent. <arg1> It </arg1> <rel> reckons </rel> <arg2> them </arg2> 1
|
| 262 |
+
It now reckons them at 3.6 percent and 2.2 percent. <arg1> them </arg1> <rel> was </rel> <arg2> 3.6 percent </arg2> 1
|
| 263 |
+
It now reckons them at 3.6 percent and 2.2 percent. <arg1> them </arg1> <rel> was </rel> <arg2> 2.2 percent </arg2> 1
|
| 264 |
+
It had the euro zone and Japan growing 1.5 percent and 0.6 percent, respectively. <arg1> It </arg1> <rel> had </rel> <arg2> the euro zone </arg2> 1
|
| 265 |
+
It had the euro zone and Japan growing 1.5 percent and 0.6 percent, respectively. <arg1> It </arg1> <rel> had </rel> <arg2> Japan </arg2> 1
|
| 266 |
+
It had the euro zone and Japan growing 1.5 percent and 0.6 percent, respectively. <arg1> It </arg1> <rel> predicted the growth of </rel> <arg2> the euro zone </arg2> 1
|
| 267 |
+
It had the euro zone and Japan growing 1.5 percent and 0.6 percent, respectively. <arg1> It </arg1> <rel> predicted the growth of </rel> <arg2> Japan </arg2> 1
|
| 268 |
+
It had the euro zone and Japan growing 1.5 percent and 0.6 percent, respectively. <arg1> the euro zone </arg1> <rel> grew </rel> <arg2> 1.5 percent </arg2> 1
|
| 269 |
+
It had the euro zone and Japan growing 1.5 percent and 0.6 percent, respectively. <arg1> Japan </arg1> <rel> grew </rel> <arg2> 0.6 percent </arg2> 1
|
| 270 |
+
It had the euro zone and Japan growing 1.5 percent and 0.6 percent, respectively. <arg1> the euro </arg1> <rel> has </rel> <arg2> a zone </arg2> 1
|
| 271 |
+
It now has them at 2.1 percent and 1.5 percent. <arg1> It </arg1> <rel> has </rel> <arg2> them </arg2> 1
|
| 272 |
+
��Faster growth is reaching roughly two-thirds of the world’s population,” the IMF said in a December blog post. <arg1> Faster growth </arg1> <rel> is reaching </rel> <arg2> two-thirds of the world ’ s population </arg2> 1
|
| 273 |
+
“Faster growth is reaching roughly two-thirds of the world’s population,” the IMF said in a December blog post. <arg1> the IMF </arg1> <rel> said </rel> <arg2> “ Faster growth is reaching roughly two-thirds of the world ’ s population , ” </arg2> 1
|
| 274 |
+
“Faster growth is reaching roughly two-thirds of the world’s population,” the IMF said in a December blog post. <arg1> the IMF </arg1> <rel> has </rel> <arg2> a blog </arg2> 1
|
| 275 |
+
This performance has made some economists optimistic. <arg1> This performance </arg1> <rel> has made </rel> <arg2> some economists </arg2> 1
|
| 276 |
+
Nomura is among the more bullish: “Global growth has far more self-reinforcing characteristics at present than at any time over the last 20-30 years.” <arg1> Nomura </arg1> <rel> is among </rel> <arg2> the more bullish </arg2> 1
|
| 277 |
+
Nomura is among the more bullish: “Global growth has far more self-reinforcing characteristics at present than at any time over the last 20-30 years.” <arg1> Nomura </arg1> <rel> is </rel> <arg2> bullish </arg2> 1
|
| 278 |
+
Nomura is among the more bullish: “Global growth has far more self-reinforcing characteristics at present than at any time over the last 20-30 years.” <arg1> Global growth </arg1> <rel> has more </rel> <arg2> self-reinforcing characteristics at present </arg2> 1
|
| 279 |
+
Nomura is among the more bullish: “Global growth has far more self-reinforcing characteristics at present than at any time over the last 20-30 years.” <arg1> Global growth </arg1> <rel> has </rel> <arg2> self-reinforcing characteristics </arg2> 1
|
| 280 |
+
There are huge numbers of potential political and economic risks to the status quo. <arg1> risks to the status quo </arg1> <rel> are </rel> <arg2> political and economic </arg2> 1
|
| 281 |
+
There are huge numbers of potential political and economic risks to the status quo. <arg1> economic risks </arg1> <rel> threaten </rel> <arg2> the status quo </arg2> 1
|
| 282 |
+
There are huge numbers of potential political and economic risks to the status quo. <arg1> potential political risks </arg1> <rel> threaten </rel> <arg2> the status quo </arg2> 1
|
| 283 |
+
There are huge numbers of potential political and economic risks to the status quo. <arg1> political risks </arg1> <rel> threaten </rel> <arg2> the status quo </arg2> 1
|
| 284 |
+
But as in the fairy tale, let’s go with just three: central banks, trade, and bubbles. <arg1> central banks </arg1> <rel> are a risk to </rel> <arg2> a year of relatively healthy global economic growth </arg2> 1
|
| 285 |
+
But as in the fairy tale, let’s go with just three: central banks, trade, and bubbles. <arg1> trade </arg1> <rel> is a risk to </rel> <arg2> a year of relatively healthy global economic growth </arg2> 1
|
| 286 |
+
But as in the fairy tale, let’s go with just three: central banks, trade, and bubbles. <arg1> bubbles </arg1> <rel> is a risk to </rel> <arg2> a year of relatively healthy global economic growth </arg2> 1
|
| 287 |
+
In the first case, the danger is that there will be a policy mistake, squeezing debtors. <arg1> a policy mistake </arg1> <rel> would squeeze </rel> <arg2> debtors </arg2> 1
|
| 288 |
+
In the first case, the danger is that there will be a policy mistake, squeezing debtors. <arg1> a policy mistake </arg1> <rel> is </rel> <arg2> a danger </arg2> 1
|
| 289 |
+
The second relates to renewed U.S. protectionism or anger over Chinese exports triggering tit-for-tat, growth-stifling trade barriers. <arg1> renewed U.S. protectionism </arg1> <rel> could trigger </rel> <arg2> trade barriers </arg2> 1
|
| 290 |
+
The second relates to renewed U.S. protectionism or anger over Chinese exports triggering tit-for-tat, growth-stifling trade barriers. <arg1> anger over Chinese exports </arg1> <rel> could trigger </rel> <arg2> trade barriers </arg2> 1
|
| 291 |
+
The second relates to renewed U.S. protectionism or anger over Chinese exports triggering tit-for-tat, growth-stifling trade barriers. <arg1> trade barriers </arg1> <rel> stifle </rel> <arg2> growth </arg2> 1
|
| 292 |
+
The third is about sudden market losses that dry up spending and demand. <arg1> The third </arg1> <rel> is about </rel> <arg2> sudden market losses </arg2> 1
|
| 293 |
+
The third is about sudden market losses that dry up spending and demand. <arg1> sudden market losses </arg1> <rel> could dry up </rel> <arg2> spending </arg2> 1
|
| 294 |
+
The third is about sudden market losses that dry up spending and demand. <arg1> sudden market losses </arg1> <rel> could dry up </rel> <arg2> demand </arg2> 1
|
evaluation_data/wire57/gold_data/wire57_test_sentences.txt
ADDED
|
@@ -0,0 +1,56 @@
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|
| 1 |
+
Tokyo ( ˈtoʊkioʊ , Japanese : toːkʲoː ) , officially Tokyo Metropolis , is the capital city of Japan and one of its 47 prefectures .
|
| 2 |
+
The Greater Tokyo Area is the most populous metropolitan area in the world .
|
| 3 |
+
It is the seat of the Emperor of Japan and the Japanese government .
|
| 4 |
+
Tokyo is in the Kantō region on the southeastern side of the main island Honshu and includes the Izu Islands and Ogasawara Islands .
|
| 5 |
+
Formerly known as Edo , it has been the de facto seat of government since 1603 when Shogun Tokugawa Ieyasu made the city his headquarters .
|
| 6 |
+
It officially became the capital after Emperor Meiji moved his seat to the city from the old capital of Kyoto in 1868 ; at that time Edo was renamed Tokyo .
|
| 7 |
+
Tokyo Metropolis was formed in 1943 from the merger of the former Tokyo Prefecture ( 東京府 Tōkyō - fu ) and the city of Tokyo ( 東京市 Tōkyō - shi ) .
|
| 8 |
+
Tokyo is often referred to as a city , but is officially known and governed as a " metropolitan prefecture " , which differs from and combines elements of a city and a prefecture , a characteristic unique to Tokyo .
|
| 9 |
+
The Tokyo metropolitan government administers the 23 Special Wards of Tokyo ( each governed as an individual city ) , which cover the area that was the City of Tokyo before it merged and became the metropolitan prefecture in 1943 .
|
| 10 |
+
The metropolitan government also administers 39 municipalities in the western part of the prefecture and the two outlying island chains .
|
| 11 |
+
The population of the special wards is over 9 million people , with the total population of the prefecture exceeding 13 million .
|
| 12 |
+
The prefecture is part of the world 's most populous metropolitan area with upwards of 37.8 million people and the world 's largest urban agglomeration economy .
|
| 13 |
+
In 2011 , the city hosted 51 of the Fortune Global 500 companies , the highest number of any city in the world , at that time .
|
| 14 |
+
Tokyo ranked third ( twice ) in the International Financial Centres Development IndexEdit .
|
| 15 |
+
The city is also home to various television networks such as Fuji TV , Tokyo MX , TV Tokyo , TV Asahi , Nippon Television , NHK and the Tokyo Broadcasting System .
|
| 16 |
+
Finnish police reprimanded a man for traveling in a car boot to hide his meeting with Prime Minister Juha Sipila during a government crisis last summer , saying this was breach of the traffic code .
|
| 17 |
+
A police statement did not name the man in the boot , but in effect indicated the traveler was State Secretary Samuli Virtanen , who is also the deputy to Foreign Minister Timo Soini .
|
| 18 |
+
The meeting took place in June , a day after Virtanen ’s co-ruling Finns party had elected anti-immigration hardliners as its new leaders .
|
| 19 |
+
The government was close to collapse until a group of politicians , including Virtanen and Soini , in the following week walked out of the Finns party and announced they would form a new group .
|
| 20 |
+
The Finns party was thrown out of the government and the new “ Blue Reform ” group kept its cabinet seat .
|
| 21 |
+
Virtanen has not commented on the case , but lawmaker Tiina Elovaara from Blue Reform said in a blog that Virtanen climbed into the boot to keep the meeting secret at a critical moment .
|
| 22 |
+
“ He avoided media attention when the situation was most serious , and the risk of leakage about the parliamentarians ’ transition was too big , ” Elovaara said .
|
| 23 |
+
Chilly Gonzales ( born Jason Charles Beck ; 20 March 1972 ) is a Grammy - winning Canadian musician who resided in Paris , France for several years , and now lives in Cologne , Germany .
|
| 24 |
+
Known for his albums of classical piano compositions with a pop music sensibility , Solo Piano I and Solo Piano II , as well as his MC and electro albums , he is also a producer and songwriter .
|
| 25 |
+
Gonzales broadcasts a web series Pop Music Masterclass on WDR , the documentary Classical Connections on BBC Radio 1 , The History of Music on Arte , and Music 's Cool with Chilly Gonzales on Apple Music 's Beats 1 radio show .
|
| 26 |
+
He has written several newspaper and magazine opinion pieces in The Guardian , Vice , Billboard , and others .
|
| 27 |
+
He is the younger brother of the prolific film composer Christophe Beck .
|
| 28 |
+
Gonzales was born on 20 March 1972 .
|
| 29 |
+
His parents are Ashkenazi Jews who had to flee from Hungary during World War II .
|
| 30 |
+
Gonzales began teaching himself piano at age three , when his older brother Chris began taking lessons .
|
| 31 |
+
Gonzales graduated from Crescent School in Toronto , Ontario , Canada .
|
| 32 |
+
He was classically trained as a pianist at McGill University , where he began both his composing career , co-authoring several musicals with his brother , and his performing career , as a jazz virtuoso .
|
| 33 |
+
In statistics , an expectation – maximization ( EM ) algorithm is an iterative method to find maximum likelihood or maximum a posteriori ( MAP ) estimates of parameters in statistical models , where the model depends on unobserved latent variables .
|
| 34 |
+
The EM iteration alternates between performing an expectation ( E ) step , which creates a function for the expectation of the log -likelihood evaluated using the current estimate for the parameters , and a maximization ( M ) step , which computes parameters maximizing the expected log - likelihood found on the E step .
|
| 35 |
+
These parameter - estimates are then used to determine the distribution of the latent variables in the next E step .
|
| 36 |
+
The EM algorithm was explained and given its name in a classic 1977 paper by Arthur Dempster , Nan Laird , and Donald Rubin .
|
| 37 |
+
They pointed out that the method had been " proposed many times in special circumstances " by earlier authors .
|
| 38 |
+
A very detailed treatment of the EM method for exponential families was published by Rolf Sundberg in his thesis and several papers following his collaboration with Per Martin - Löf and Anders Martin - Löf .
|
| 39 |
+
The Dempster – Laird–Rubin paper in 1977 generalized the method and sketched a convergence analysis for a wider class of problems .
|
| 40 |
+
Regardless of earlier inventions , the innovative Dempster – Laird – Rubin paper in the Journal of the Royal Statistical Society received an enthusiastic discussion at the Royal Statistical Society meeting with Sundberg calling the paper " brilliant " .
|
| 41 |
+
The Dempster – Laird –Rubin paper established the EM method as an important tool of statistical analysis .
|
| 42 |
+
After a year of relatively healthy global economic growth , economists are predicting pretty much the same for 2018 -- a neither too - hot nor too - cold Goldilocks scenario , but with little sight of the three bears .
|
| 43 |
+
The idea is that all is pretty much on track for growth that will be stronger than in 2017 .
|
| 44 |
+
Part of this may come from the fact that forecasters generally got it wrong last year , underclubbing this year ’s economic performance , particularly for the euro zone and Japan .
|
| 45 |
+
The International Monetary Fund , for example , saw 2017 global growth at 3.4 percent with advanced economies advancing 1.8 percent .
|
| 46 |
+
It now reckons them at 3.6 percent and 2.2 percent .
|
| 47 |
+
It had the euro zone and Japan growing 1.5 percent and 0.6 percent , respectively .
|
| 48 |
+
It now has them at 2.1 percent and 1.5 percent .
|
| 49 |
+
“ Faster growth is reaching roughly two - thirds of the world ’s population , ” the IMF said in a December blog post .
|
| 50 |
+
This performance has made some economists optimistic .
|
| 51 |
+
Nomura is among the more bullish : “ Global growth has far more self - reinforcing characteristics at present than at any time over the last 20 - 30 years . ”
|
| 52 |
+
There are huge numbers of potential political and economic risks to the status quo .
|
| 53 |
+
But as in the fairy tale , let ’s go with just three : central banks , trade , and bubbles .
|
| 54 |
+
In the first case , the danger is that there will be a policy mistake , squeezing debtors .
|
| 55 |
+
The second relates to renewed U.S. protectionism or anger over Chinese exports triggering tit - for - tat , growth - stifling trade barriers .
|
| 56 |
+
The third is about sudden market losses that dry up spending and demand .
|
evaluation_data/wire57/wire57_conjunctions.txt
ADDED
|
@@ -0,0 +1,236 @@
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| 1 |
+
Tokyo ( ˈtoʊkioʊ , Japanese : toːkʲoː ) , officially Tokyo Metropolis , is the capital city of Japan and one of its 47 prefectures .
|
| 2 |
+
Tokyo ( ˈtoʊkioʊ , Japanese : toːkʲoː ) , officially Tokyo Metropolis , is the capital city of Japan .
|
| 3 |
+
Tokyo ( ˈtoʊkioʊ , Japanese : toːkʲoː ) , officially Tokyo Metropolis , is one of its 47 prefectures .
|
| 4 |
+
|
| 5 |
+
The Greater Tokyo Area is the most populous metropolitan area in the world .
|
| 6 |
+
|
| 7 |
+
|
| 8 |
+
It is the seat of the Emperor of Japan and the Japanese government .
|
| 9 |
+
It is the seat of the Emperor of Japan .
|
| 10 |
+
It is the seat of the Japanese government .
|
| 11 |
+
|
| 12 |
+
Tokyo is in the Kantō region on the southeastern side of the main island Honshu and includes the Izu Islands and Ogasawara Islands .
|
| 13 |
+
Tokyo is in the Kantō region on the southeastern side of the main island Honshu .
|
| 14 |
+
Tokyo includes the Izu Islands .
|
| 15 |
+
Tokyo includes Ogasawara Islands .
|
| 16 |
+
|
| 17 |
+
Formerly known as Edo , it has been the de facto seat of government since 1603 when Shogun Tokugawa Ieyasu made the city his headquarters .
|
| 18 |
+
|
| 19 |
+
|
| 20 |
+
It officially became the capital after Emperor Meiji moved his seat to the city from the old capital of Kyoto in 1868 ; at that time Edo was renamed Tokyo .
|
| 21 |
+
|
| 22 |
+
|
| 23 |
+
Tokyo Metropolis was formed in 1943 from the merger of the former Tokyo Prefecture ( 東京府 Tōkyō - fu ) and the city of Tokyo ( 東京市 Tōkyō - shi ) .
|
| 24 |
+
Tokyo Metropolis was formed in 1943 from the merger of the former Tokyo Prefecture ( 東京府 Tōkyō - fu ) .
|
| 25 |
+
Tokyo Metropolis was formed in 1943 from the merger of the city of Tokyo ( 東京市 Tōkyō - shi ) .
|
| 26 |
+
|
| 27 |
+
Tokyo is often referred to as a city , but is officially known and governed as a " metropolitan prefecture " , which differs from and combines elements of a city and a prefecture , a characteristic unique to Tokyo .
|
| 28 |
+
Tokyo is often referred to as a city .
|
| 29 |
+
Tokyo is officially known as a " metropolitan prefecture " , which differs from of a city , a characteristic unique to Tokyo .
|
| 30 |
+
Tokyo is officially known as a " metropolitan prefecture " , which differs from of a prefecture , a characteristic unique to Tokyo .
|
| 31 |
+
Tokyo is officially known as a " metropolitan prefecture " , which combines elements of a city , a characteristic unique to Tokyo .
|
| 32 |
+
Tokyo is officially known as a " metropolitan prefecture " , which combines elements of a prefecture , a characteristic unique to Tokyo .
|
| 33 |
+
Tokyo is officially governed as a " metropolitan prefecture " , which differs from of a city , a characteristic unique to Tokyo .
|
| 34 |
+
Tokyo is officially governed as a " metropolitan prefecture " , which differs from of a prefecture , a characteristic unique to Tokyo .
|
| 35 |
+
Tokyo is officially governed as a " metropolitan prefecture " , which combines elements of a city , a characteristic unique to Tokyo .
|
| 36 |
+
Tokyo is officially governed as a " metropolitan prefecture " , which combines elements of a prefecture , a characteristic unique to Tokyo .
|
| 37 |
+
|
| 38 |
+
The Tokyo metropolitan government administers the 23 Special Wards of Tokyo ( each governed as an individual city ) , which cover the area that was the City of Tokyo before it merged and became the metropolitan prefecture in 1943 .
|
| 39 |
+
The Tokyo metropolitan government administers the 23 Special Wards of Tokyo ( each governed as an individual city ) , which cover the area that was the City of Tokyo before it merged .
|
| 40 |
+
The Tokyo metropolitan government administers the 23 Special Wards of Tokyo ( each governed as an individual city ) , which cover the area that was the City of Tokyo before it became the metropolitan prefecture in 1943 .
|
| 41 |
+
|
| 42 |
+
The metropolitan government also administers 39 municipalities in the western part of the prefecture and the two outlying island chains .
|
| 43 |
+
The metropolitan government also administers 39 municipalities in the western part of the prefecture .
|
| 44 |
+
The metropolitan government also administers 39 municipalities in the two outlying island chains .
|
| 45 |
+
|
| 46 |
+
The population of the special wards is over 9 million people , with the total population of the prefecture exceeding 13 million .
|
| 47 |
+
|
| 48 |
+
|
| 49 |
+
The prefecture is part of the world 's most populous metropolitan area with upwards of 37.8 million people and the world 's largest urban agglomeration economy .
|
| 50 |
+
The prefecture is part of the world 's most populous metropolitan area with upwards of 37.8 million people .
|
| 51 |
+
The prefecture is part of the world 's most populous metropolitan area with the world 's largest urban agglomeration economy .
|
| 52 |
+
|
| 53 |
+
In 2011 , the city hosted 51 of the Fortune Global 500 companies , the highest number of any city in the world , at that time .
|
| 54 |
+
|
| 55 |
+
|
| 56 |
+
Tokyo ranked third ( twice ) in the International Financial Centres Development IndexEdit .
|
| 57 |
+
|
| 58 |
+
|
| 59 |
+
The city is also home to various television networks such as Fuji TV , Tokyo MX , TV Tokyo , TV Asahi , Nippon Television , NHK and the Tokyo Broadcasting System .
|
| 60 |
+
The city is also home to various television networks such as Fuji TV .
|
| 61 |
+
The city is also home to various television networks such as Tokyo MX .
|
| 62 |
+
The city is also home to various television networks such as TV Tokyo .
|
| 63 |
+
The city is also home to various television networks such as TV Asahi .
|
| 64 |
+
The city is also home to various television networks such as Nippon Television .
|
| 65 |
+
The city is also home to various television networks such as NHK .
|
| 66 |
+
The city is also home to various television networks such as the Tokyo Broadcasting System .
|
| 67 |
+
|
| 68 |
+
Finnish police reprimanded a man for traveling in a car boot to hide his meeting with Prime Minister Juha Sipila during a government crisis last summer , saying this was breach of the traffic code .
|
| 69 |
+
|
| 70 |
+
|
| 71 |
+
A police statement did not name the man in the boot , but in effect indicated the traveler was State Secretary Samuli Virtanen , who is also the deputy to Foreign Minister Timo Soini .
|
| 72 |
+
A police statement did not name the man in the boot .
|
| 73 |
+
A police statement in effect indicated the traveler was State Secretary Samuli Virtanen , who is also the deputy to Foreign Minister Timo Soini .
|
| 74 |
+
|
| 75 |
+
The meeting took place in June , a day after Virtanen 's co-ruling Finns party had elected anti-immigration hardliners as its new leaders .
|
| 76 |
+
|
| 77 |
+
|
| 78 |
+
The government was close to collapse until a group of politicians , including Virtanen and Soini , in the following week walked out of the Finns party and announced they would form a new group .
|
| 79 |
+
The government was close to collapse until a group of politicians , including Virtanen , in the following week walked out of the Finns party .
|
| 80 |
+
The government was close to collapse until a group of politicians , including Virtanen , in the following week announced they would form a new group .
|
| 81 |
+
The government was close to collapse until a group of politicians , including Soini , in the following week walked out of the Finns party .
|
| 82 |
+
The government was close to collapse until a group of politicians , including Soini , in the following week announced they would form a new group .
|
| 83 |
+
|
| 84 |
+
The Finns party was thrown out of the government and the new '' Blue Reform '' group kept its cabinet seat .
|
| 85 |
+
The Finns party was thrown out of the government .
|
| 86 |
+
the new '' Blue Reform '' group kept its cabinet seat .
|
| 87 |
+
|
| 88 |
+
Virtanen has not commented on the case , but lawmaker Tiina Elovaara from Blue Reform said in a blog that Virtanen climbed into the boot to keep the meeting secret at a critical moment .
|
| 89 |
+
Virtanen has not commented on the case .
|
| 90 |
+
lawmaker Tiina Elovaara from Blue Reform said in a blog that Virtanen climbed into the boot to keep the meeting secret at a critical moment .
|
| 91 |
+
|
| 92 |
+
'' He avoided media attention when the situation was most serious , and the risk of leakage about the parliamentarians ' transition was too big , '' Elovaara said .
|
| 93 |
+
'' He avoided media attention when the situation was most serious , '' Elovaara said .
|
| 94 |
+
'' He avoided media attention when the risk of leakage about the parliamentarians ' transition was too big , '' Elovaara said .
|
| 95 |
+
|
| 96 |
+
Chilly Gonzales ( born Jason Charles Beck ; 20 March 1972 ) is a Grammy - winning Canadian musician who resided in Paris , France for several years , and now lives in Cologne , Germany .
|
| 97 |
+
Chilly Gonzales ( born Jason Charles Beck ; 20 March 1972 ) is a Grammy - winning Canadian musician who resided in Paris , France for several years .
|
| 98 |
+
Chilly Gonzales ( born Jason Charles Beck ; 20 March 1972 ) is a Grammy - winning Canadian musician who now lives in Cologne , Germany .
|
| 99 |
+
|
| 100 |
+
Known for his albums of classical piano compositions with a pop music sensibility , Solo Piano I and Solo Piano II , as well as his MC and electro albums , he is also a producer and songwriter .
|
| 101 |
+
Known for his albums of classical piano compositions with a pop music sensibility , Solo Piano I , as well as his MC albums , he is also a producer .
|
| 102 |
+
Known for his albums of classical piano compositions with a pop music sensibility , Solo Piano I , as well as his MC albums , he is also a songwriter .
|
| 103 |
+
Known for his albums of classical piano compositions with a pop music sensibility , Solo Piano I , as well as his electro albums , he is also a producer .
|
| 104 |
+
Known for his albums of classical piano compositions with a pop music sensibility , Solo Piano I , as well as his electro albums , he is also a songwriter .
|
| 105 |
+
Known for his albums of classical piano compositions with a pop music sensibility , Solo Piano II , as well as his MC albums , he is also a producer .
|
| 106 |
+
Known for his albums of classical piano compositions with a pop music sensibility , Solo Piano II , as well as his MC albums , he is also a songwriter .
|
| 107 |
+
Known for his albums of classical piano compositions with a pop music sensibility , Solo Piano II , as well as his electro albums , he is also a producer .
|
| 108 |
+
Known for his albums of classical piano compositions with a pop music sensibility , Solo Piano II , as well as his electro albums , he is also a songwriter .
|
| 109 |
+
|
| 110 |
+
Gonzales broadcasts a web series Pop Music Masterclass on WDR , the documentary Classical Connections on BBC Radio 1 , The History of Music on Arte , and Music 's Cool with Chilly Gonzales on Apple Music 's Beats 1 radio show .
|
| 111 |
+
Gonzales broadcasts a web series Pop Music Masterclass on WDR .
|
| 112 |
+
Gonzales broadcasts the documentary Classical Connections on BBC Radio 1 .
|
| 113 |
+
Gonzales broadcasts The History of Music on Arte .
|
| 114 |
+
Gonzales broadcasts Music 's Cool with Chilly Gonzales on Apple Music 's Beats 1 radio show .
|
| 115 |
+
|
| 116 |
+
He has written several newspaper and magazine opinion pieces in The Guardian , Vice , Billboard , and others .
|
| 117 |
+
He has written several newspaper opinion pieces in The Guardian .
|
| 118 |
+
He has written several newspaper opinion pieces in Vice .
|
| 119 |
+
He has written several newspaper opinion pieces in Billboard .
|
| 120 |
+
He has written several newspaper opinion pieces in others .
|
| 121 |
+
He has written several magazine opinion pieces in The Guardian .
|
| 122 |
+
He has written several magazine opinion pieces in Vice .
|
| 123 |
+
He has written several magazine opinion pieces in Billboard .
|
| 124 |
+
He has written several magazine opinion pieces in others .
|
| 125 |
+
|
| 126 |
+
He is the younger brother of the prolific film composer Christophe Beck .
|
| 127 |
+
|
| 128 |
+
|
| 129 |
+
Gonzales was born on 20 March 1972 .
|
| 130 |
+
|
| 131 |
+
|
| 132 |
+
His parents are Ashkenazi Jews who had to flee from Hungary during World War II .
|
| 133 |
+
|
| 134 |
+
|
| 135 |
+
Gonzales began teaching himself piano at age three , when his older brother Chris began taking lessons .
|
| 136 |
+
|
| 137 |
+
|
| 138 |
+
Gonzales graduated from Crescent School in Toronto , Ontario , Canada .
|
| 139 |
+
|
| 140 |
+
|
| 141 |
+
He was classically trained as a pianist at McGill University , where he began both his composing career , co-authoring several musicals with his brother , and his performing career , as a jazz virtuoso .
|
| 142 |
+
He was classically trained as a pianist at McGill University , where he began both his composing career , co-authoring several musicals with his brother .
|
| 143 |
+
He was classically trained as a pianist at McGill University , where he began both his performing career , as a jazz virtuoso .
|
| 144 |
+
|
| 145 |
+
In statistics , an expectation – maximization ( EM ) algorithm is an iterative method to find maximum likelihood or maximum a posteriori ( MAP ) estimates of parameters in statistical models , where the model depends on unobserved latent variables .
|
| 146 |
+
In statistics , an expectation – maximization ( EM ) algorithm is an iterative method to find maximum likelihood ) estimates of parameters in statistical models , where the model depends on unobserved latent variables .
|
| 147 |
+
In statistics , an expectation – maximization ( EM ) algorithm is an iterative method to find maximum a posteriori ( MAP ) estimates of parameters in statistical models , where the model depends on unobserved latent variables .
|
| 148 |
+
|
| 149 |
+
The EM iteration alternates between performing an expectation ( E ) step , which creates a function for the expectation of the log -likelihood evaluated using the current estimate for the parameters , and a maximization ( M ) step , which computes parameters maximizing the expected log - likelihood found on the E step .
|
| 150 |
+
|
| 151 |
+
|
| 152 |
+
These parameter - estimates are then used to determine the distribution of the latent variables in the next E step .
|
| 153 |
+
|
| 154 |
+
|
| 155 |
+
The EM algorithm was explained and given its name in a classic 1977 paper by Arthur Dempster , Nan Laird , and Donald Rubin .
|
| 156 |
+
The EM algorithm was explained in a classic 1977 paper by Arthur Dempster .
|
| 157 |
+
The EM algorithm was explained in a classic 1977 paper by Nan Laird .
|
| 158 |
+
The EM algorithm was explained in a classic 1977 paper by Donald Rubin .
|
| 159 |
+
The EM algorithm was given its name in a classic 1977 paper by Arthur Dempster .
|
| 160 |
+
The EM algorithm was given its name in a classic 1977 paper by Nan Laird .
|
| 161 |
+
The EM algorithm was given its name in a classic 1977 paper by Donald Rubin .
|
| 162 |
+
|
| 163 |
+
They pointed out that the method had been " proposed many times in special circumstances " by earlier authors .
|
| 164 |
+
|
| 165 |
+
|
| 166 |
+
A very detailed treatment of the EM method for exponential families was published by Rolf Sundberg in his thesis and several papers following his collaboration with Per Martin - Löf and Anders Martin - Löf .
|
| 167 |
+
A very detailed treatment of the EM method for exponential families was published by Rolf Sundberg in his thesis .
|
| 168 |
+
A very detailed treatment of the EM method for exponential families was published by Rolf Sundberg in several papers following his collaboration with Per Martin - Löf .
|
| 169 |
+
A very detailed treatment of the EM method for exponential families was published by Rolf Sundberg in several papers following his collaboration with Anders Martin - Löf .
|
| 170 |
+
|
| 171 |
+
The Dempster – Laird–Rubin paper in 1977 generalized the method and sketched a convergence analysis for a wider class of problems .
|
| 172 |
+
The Dempster – Laird–Rubin paper in 1977 generalized the method .
|
| 173 |
+
The Dempster – Laird–Rubin paper in 1977 sketched a convergence analysis for a wider class of problems .
|
| 174 |
+
|
| 175 |
+
Regardless of earlier inventions , the innovative Dempster – Laird – Rubin paper in the Journal of the Royal Statistical Society received an enthusiastic discussion at the Royal Statistical Society meeting with Sundberg calling the paper " brilliant " .
|
| 176 |
+
|
| 177 |
+
|
| 178 |
+
The Dempster – Laird –Rubin paper established the EM method as an important tool of statistical analysis .
|
| 179 |
+
|
| 180 |
+
|
| 181 |
+
After a year of relatively healthy global economic growth , economists are predicting pretty much the same for 2018 -- a neither too - hot nor too - cold Goldilocks scenario , but with little sight of the three bears .
|
| 182 |
+
|
| 183 |
+
|
| 184 |
+
The idea is that all is pretty much on track for growth that will be stronger than in 2017 .
|
| 185 |
+
|
| 186 |
+
|
| 187 |
+
Part of this may come from the fact that forecasters generally got it wrong last year , underclubbing this year 's economic performance , particularly for the euro zone and Japan .
|
| 188 |
+
Part of this may come from the fact that forecasters generally got it wrong last year , underclubbing this year 's economic performance , particularly for the euro zone .
|
| 189 |
+
Part of this may come from the fact that forecasters generally got it wrong last year , underclubbing this year 's economic performance , particularly for Japan .
|
| 190 |
+
|
| 191 |
+
The International Monetary Fund , for example , saw 2017 global growth at 3.4 percent with advanced economies advancing 1.8 percent .
|
| 192 |
+
|
| 193 |
+
|
| 194 |
+
It now reckons them at 3.6 percent and 2.2 percent .
|
| 195 |
+
It now reckons them at 3.6 percent .
|
| 196 |
+
It now reckons them at 2.2 percent .
|
| 197 |
+
|
| 198 |
+
It had the euro zone and Japan growing 1.5 percent and 0.6 percent , respectively .
|
| 199 |
+
It had the euro zone growing 1.5 percent , respectively .
|
| 200 |
+
It had the euro zone growing 0.6 percent , respectively .
|
| 201 |
+
It had Japan growing 1.5 percent , respectively .
|
| 202 |
+
It had Japan growing 0.6 percent , respectively .
|
| 203 |
+
|
| 204 |
+
It now has them at 2.1 percent and 1.5 percent .
|
| 205 |
+
It now has them at 2.1 percent .
|
| 206 |
+
It now has them at 1.5 percent .
|
| 207 |
+
|
| 208 |
+
'' Faster growth is reaching roughly two - thirds of the world 's population , '' the IMF said in a December blog post .
|
| 209 |
+
|
| 210 |
+
|
| 211 |
+
This performance has made some economists optimistic .
|
| 212 |
+
|
| 213 |
+
|
| 214 |
+
Nomura is among the more bullish : '' Global growth has far more self - reinforcing characteristics at present than at any time over the last 20 - 30 years . ''
|
| 215 |
+
|
| 216 |
+
|
| 217 |
+
There are huge numbers of potential political and economic risks to the status quo .
|
| 218 |
+
There are huge numbers of potential political risks to the status quo .
|
| 219 |
+
There are huge numbers of potential economic risks to the status quo .
|
| 220 |
+
|
| 221 |
+
But as in the fairy tale , let 's go with just three : central banks , trade , and bubbles .
|
| 222 |
+
But as in the fairy tale , let 's go with just three : central banks .
|
| 223 |
+
But as in the fairy tale , let 's go with just three : trade .
|
| 224 |
+
But as in the fairy tale , let 's go with just three : bubbles .
|
| 225 |
+
|
| 226 |
+
In the first case , the danger is that there will be a policy mistake , squeezing debtors .
|
| 227 |
+
|
| 228 |
+
|
| 229 |
+
The second relates to renewed U.S. protectionism or anger over Chinese exports triggering tit - for - tat , growth - stifling trade barriers .
|
| 230 |
+
The second relates to renewed U.S. protectionism triggering tit - for - tat , growth - stifling trade barriers .
|
| 231 |
+
The second relates to anger over Chinese exports triggering tit - for - tat , growth - stifling trade barriers .
|
| 232 |
+
|
| 233 |
+
The third is about sudden market losses that dry up spending and demand .
|
| 234 |
+
The third is about sudden market losses that dry up spending .
|
| 235 |
+
The third is about sudden market losses that dry up demand .
|
| 236 |
+
|
evaluation_data/wire57/wire57_evaluation.py
ADDED
|
@@ -0,0 +1,304 @@
|
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|
| 1 |
+
import json
|
| 2 |
+
import re
|
| 3 |
+
import argparse
|
| 4 |
+
|
| 5 |
+
|
| 6 |
+
def load_WiRe_annotations():
|
| 7 |
+
save_path = "../data/WiRe57_343-manual-oie.json"
|
| 8 |
+
annotations = json.load(open(save_path))
|
| 9 |
+
return annotations
|
| 10 |
+
|
| 11 |
+
|
| 12 |
+
def get_extraction_wire57(arg1, rel, arg2):
|
| 13 |
+
return {'arg1': arg1, 'rel': rel, 'arg2': arg2}
|
| 14 |
+
|
| 15 |
+
|
| 16 |
+
def get_extraction_wire57_gold(arg1, rel, arg2):
|
| 17 |
+
extraction = {}
|
| 18 |
+
extraction['arg1'] = {'text': arg1, 'words': arg1.split()}
|
| 19 |
+
extraction['rel'] = {'text': rel, 'words': rel.split()}
|
| 20 |
+
extraction['arg2'] = {'text': arg2, 'words': arg2.split()}
|
| 21 |
+
return extraction
|
| 22 |
+
|
| 23 |
+
|
| 24 |
+
def get_allenlp_args(line):
|
| 25 |
+
assert len(re.findall("<arg1>.*</arg1>", line)) == 1
|
| 26 |
+
assert len(re.findall("<rel>.*</rel>", line)) == 1
|
| 27 |
+
assert len(re.findall("<arg2>.*</arg2>", line)) == 1
|
| 28 |
+
|
| 29 |
+
arg1 = re.findall("<arg1>.*</arg1>", line)[0].strip('<arg1>').strip('</arg1>').strip()
|
| 30 |
+
rel = re.findall("<rel>.*</rel>", line)[0].strip('<rel>').strip('</rel>').strip()
|
| 31 |
+
arg2 = re.findall("<arg2>.*</arg2>", line)[0].strip('<arg2>').strip('</arg2>').strip()
|
| 32 |
+
|
| 33 |
+
return arg1, rel, arg2
|
| 34 |
+
|
| 35 |
+
|
| 36 |
+
def process_allennlp_format(file, gold=False):
|
| 37 |
+
with open(file, 'r') as f:
|
| 38 |
+
lines = f.readlines()
|
| 39 |
+
|
| 40 |
+
extractions = {}
|
| 41 |
+
|
| 42 |
+
current_sentence = None
|
| 43 |
+
for l in lines:
|
| 44 |
+
if len(l.strip()) > 0:
|
| 45 |
+
items = l.strip().split('\t')
|
| 46 |
+
assert len(items) == 3
|
| 47 |
+
if current_sentence != items[0]:
|
| 48 |
+
current_sentence = items[0]
|
| 49 |
+
extractions[current_sentence] = []
|
| 50 |
+
arg1, rel, arg2 = get_allenlp_args(items[1])
|
| 51 |
+
if gold:
|
| 52 |
+
extr = get_extraction_wire57_gold(arg1, rel, arg2)
|
| 53 |
+
else:
|
| 54 |
+
extr = get_extraction_wire57(arg1, rel, arg2)
|
| 55 |
+
extractions[current_sentence].append(extr)
|
| 56 |
+
|
| 57 |
+
return extractions
|
| 58 |
+
|
| 59 |
+
|
| 60 |
+
def main(arguments):
|
| 61 |
+
|
| 62 |
+
gold = process_allennlp_format(arguments.gold, gold=True)
|
| 63 |
+
|
| 64 |
+
predictions_by_OIE = process_allennlp_format(arguments.system)
|
| 65 |
+
|
| 66 |
+
report = ""
|
| 67 |
+
metrics, raw_match_scores = eval_system(gold, predictions_by_OIE)
|
| 68 |
+
|
| 69 |
+
# with open("raw_scores/"+e+"_prec_scores.dat", "w") as f:
|
| 70 |
+
# f.write(str(raw_match_scores[0]))
|
| 71 |
+
# with open("raw_scores/"+e+"_rec_scores.dat", "w") as f:
|
| 72 |
+
# f.write(str(raw_match_scores[1]))
|
| 73 |
+
prec, rec = metrics['precision'], metrics['recall']
|
| 74 |
+
f1_score = f1(prec, rec)
|
| 75 |
+
exactmatch_prec = metrics['exactmatches_precision'][0] / metrics['exactmatches_precision'][1]
|
| 76 |
+
exactmatch_rec = metrics['exactmatches_recall'][0] / metrics['exactmatches_recall'][1]
|
| 77 |
+
report += ("prec/rec/f1: {:.1%} {:.1%} {:.3f}"
|
| 78 |
+
.format(prec, rec, f1_score))
|
| 79 |
+
report += ("\nprec/rec of matches only (non-matches): {:.0%} {:.0%} ({})"
|
| 80 |
+
.format(metrics['precision_of_matches'], metrics['recall_of_matches'], metrics['matches']))
|
| 81 |
+
report += ("\n{} were exactly correct, out of {} predicted / the reference {}."
|
| 82 |
+
.format(metrics['exactmatches_precision'][0],
|
| 83 |
+
metrics['exactmatches_precision'][1], metrics['exactmatches_recall'][1]))
|
| 84 |
+
report += ("\nExact-match prec/rec/f1: {:.1%} {:.1%} {:.3f}"
|
| 85 |
+
.format(exactmatch_prec, exactmatch_rec, f1(exactmatch_prec, exactmatch_rec)))
|
| 86 |
+
|
| 87 |
+
# prec, rec = metrics['precision'], metrics['recall']
|
| 88 |
+
# f1_score = f1(prec, rec)
|
| 89 |
+
#
|
| 90 |
+
# report += ("prec/rec/f1: {:.1%} {:.1%} {:.3f}".format(prec, rec, f1_score))
|
| 91 |
+
|
| 92 |
+
print(report)
|
| 93 |
+
|
| 94 |
+
def eval_system(gold, predictions):
|
| 95 |
+
results = {}
|
| 96 |
+
# Get a manytuples-to-manytuples match-score for each sentence,
|
| 97 |
+
# then gather the scores across sentences and compute the weighted-average
|
| 98 |
+
for s, reference_tuples in gold.items():
|
| 99 |
+
predicted_tuples = predictions.get(s, [])
|
| 100 |
+
results[s] = sentence_match(reference_tuples, predicted_tuples)
|
| 101 |
+
|
| 102 |
+
prec_num, prec_denom = 0, 0
|
| 103 |
+
rec_num, rec_denom = 0, 0
|
| 104 |
+
exactmatches_precnum, exactmatches_precdenom = 0,0
|
| 105 |
+
exactmatches_recnum, exactmatches_recdenom = 0,0
|
| 106 |
+
tot_prec_of_matches, tot_rec_of_matches = 0, 0
|
| 107 |
+
|
| 108 |
+
for s in results.values():
|
| 109 |
+
prec_num += s['precision'][0]
|
| 110 |
+
prec_denom += s['precision'][1]
|
| 111 |
+
rec_num += s['recall'][0]
|
| 112 |
+
rec_denom += s['recall'][1]
|
| 113 |
+
exactmatches_precnum += s['exact_match_precision'][0]
|
| 114 |
+
exactmatches_precdenom += s['exact_match_precision'][1]
|
| 115 |
+
exactmatches_recnum += s['exact_match_recall'][0]
|
| 116 |
+
exactmatches_recdenom += s['exact_match_recall'][1]
|
| 117 |
+
tot_prec_of_matches += sum(s['precision_of_matches'])
|
| 118 |
+
tot_rec_of_matches += sum(s['recall_of_matches'])
|
| 119 |
+
|
| 120 |
+
precision_scores = [v for s in results.values() for v in s['precision_of_matches']]
|
| 121 |
+
recall_scores = [v for s in results.values() for v in s['recall_of_matches']]
|
| 122 |
+
raw_match_scores = [precision_scores, recall_scores]
|
| 123 |
+
matches = len(precision_scores)
|
| 124 |
+
|
| 125 |
+
metrics = {
|
| 126 |
+
'precision': prec_num / prec_denom,
|
| 127 |
+
'recall': rec_num / rec_denom,
|
| 128 |
+
'matches': matches,
|
| 129 |
+
'precision_of_matches': tot_prec_of_matches / matches,
|
| 130 |
+
'recall_of_matches': tot_rec_of_matches / matches,
|
| 131 |
+
'exactmatches_precision': [exactmatches_precnum, exactmatches_precdenom],
|
| 132 |
+
'exactmatches_recall': [exactmatches_recnum, exactmatches_recdenom]
|
| 133 |
+
}
|
| 134 |
+
# raw_match_scores = None
|
| 135 |
+
return metrics, raw_match_scores
|
| 136 |
+
|
| 137 |
+
|
| 138 |
+
# TODO:
|
| 139 |
+
# - Implement half points for part-misplaced words.
|
| 140 |
+
# - Deal with prepositions possibly being the first token of an arg, especially for arg2.
|
| 141 |
+
# > It's fully ok for "any" prep to be last word of ref_rel or first_word of pred_arg
|
| 142 |
+
|
| 143 |
+
|
| 144 |
+
def avg(l):
|
| 145 |
+
return sum(l) / len(l)
|
| 146 |
+
|
| 147 |
+
|
| 148 |
+
def f1(prec, rec):
|
| 149 |
+
try:
|
| 150 |
+
return 2 * prec * rec / (prec + rec)
|
| 151 |
+
except ZeroDivisionError:
|
| 152 |
+
return 0
|
| 153 |
+
|
| 154 |
+
|
| 155 |
+
def sentence_match(gold, predicted):
|
| 156 |
+
"""For a given sentence, compute tuple-tuple matching scores, and gather them
|
| 157 |
+
at the sentence level. Return scoring metrics."""
|
| 158 |
+
score, maximum_score = 0, len(gold)
|
| 159 |
+
exact_match_scores = [[None for _ in predicted] for __ in gold]
|
| 160 |
+
scores = [[None for _ in predicted] for __ in gold]
|
| 161 |
+
for i, gt in enumerate(gold):
|
| 162 |
+
for j, pt in enumerate(predicted):
|
| 163 |
+
exact_match_scores[i][j] = tuple_exact_match(pt, gt)
|
| 164 |
+
scores[i][j] = tuple_match(pt, gt) # this is a pair [prec,rec] or False
|
| 165 |
+
scoring_metrics = aggregate_scores_greedily(scores)
|
| 166 |
+
exact_match_summary = aggregate_exact_matches(exact_match_scores)
|
| 167 |
+
scoring_metrics['exact_match_precision'] = exact_match_summary['precision']
|
| 168 |
+
scoring_metrics['exact_match_recall'] = exact_match_summary['recall']
|
| 169 |
+
|
| 170 |
+
return scoring_metrics
|
| 171 |
+
|
| 172 |
+
|
| 173 |
+
def str_list(thing):
|
| 174 |
+
return "\n".join([str(s) for s in thing])
|
| 175 |
+
|
| 176 |
+
|
| 177 |
+
def aggregate_scores_greedily(scores):
|
| 178 |
+
# Greedy match: pick the prediction/gold match with the best f1 and exclude
|
| 179 |
+
# them both, until nothing left matches. Each input square is a [prec, rec]
|
| 180 |
+
# pair. Returns precision and recall as score-and-denominator pairs.
|
| 181 |
+
matches = []
|
| 182 |
+
while True:
|
| 183 |
+
max_s = 0
|
| 184 |
+
gold, pred = None, None
|
| 185 |
+
for i, gold_ss in enumerate(scores):
|
| 186 |
+
if i in [m[0] for m in matches]:
|
| 187 |
+
# Those are already taken rows
|
| 188 |
+
continue
|
| 189 |
+
for j, pred_s in enumerate(scores[i]):
|
| 190 |
+
if j in [m[1] for m in matches]:
|
| 191 |
+
# Those are used columns
|
| 192 |
+
continue
|
| 193 |
+
if pred_s and f1(*pred_s) > max_s:
|
| 194 |
+
max_s = f1(*pred_s)
|
| 195 |
+
gold = i
|
| 196 |
+
pred = j
|
| 197 |
+
if max_s == 0:
|
| 198 |
+
break
|
| 199 |
+
matches.append([gold, pred])
|
| 200 |
+
# Now that matches are determined, compute final scores.
|
| 201 |
+
prec_scores = [scores[i][j][0] for i, j in matches]
|
| 202 |
+
rec_scores = [scores[i][j][1] for i, j in matches]
|
| 203 |
+
total_prec = sum(prec_scores)
|
| 204 |
+
total_rec = sum(rec_scores)
|
| 205 |
+
scoring_metrics = {"precision": [total_prec, len(scores[0])],
|
| 206 |
+
"recall": [total_rec, len(scores)],
|
| 207 |
+
"precision_of_matches": prec_scores,
|
| 208 |
+
"recall_of_matches": rec_scores
|
| 209 |
+
}
|
| 210 |
+
# print(scoring_metrics)
|
| 211 |
+
return scoring_metrics
|
| 212 |
+
|
| 213 |
+
|
| 214 |
+
def aggregate_exact_matches(match_matrix):
|
| 215 |
+
# For this agregation task, no predicted tuple can exact-match two gold
|
| 216 |
+
# ones, so it's easy, look at lines and columns looking for OR-total booleans.
|
| 217 |
+
recall = [sum([any(gold_matches) for gold_matches in match_matrix], 0), len(match_matrix)]
|
| 218 |
+
# ^ this is [3,5] for "3 out of 5", to be lumped together later.
|
| 219 |
+
if len(match_matrix[0]) == 0:
|
| 220 |
+
precision = [0, 0] # N/A
|
| 221 |
+
else:
|
| 222 |
+
precision = [sum([any([g[i] for g in match_matrix]) for i in range(len(match_matrix[0]))], 0),
|
| 223 |
+
len(match_matrix[0])]
|
| 224 |
+
# f1 = 2 * precision * recall / (precision + recall)
|
| 225 |
+
metrics = {'precision': precision,
|
| 226 |
+
'recall': recall}
|
| 227 |
+
return metrics
|
| 228 |
+
|
| 229 |
+
|
| 230 |
+
def part_to_string(p):
|
| 231 |
+
return " ".join(p['words'])
|
| 232 |
+
|
| 233 |
+
|
| 234 |
+
def gold_to_text(gt):
|
| 235 |
+
text = " ; ".join([part_to_string(gt['arg1']), part_to_string(gt['rel']), part_to_string(gt['arg2'])])
|
| 236 |
+
if gt['arg3+']:
|
| 237 |
+
text += " ; " + " ; ".join(gt['arg3+'])
|
| 238 |
+
return text
|
| 239 |
+
|
| 240 |
+
|
| 241 |
+
def tuple_exact_match(t, gt):
|
| 242 |
+
"""Without resolving coref and WITH the need to hallucinate humanly infered
|
| 243 |
+
words, does the tuple match the reference ? Returns a boolean."""
|
| 244 |
+
for part in ['arg1', 'rel', 'arg2']:
|
| 245 |
+
if not t[part] == ' '.join(gt[part]['words']):
|
| 246 |
+
# This purposedly ignores that some of the gt words are 'inf'
|
| 247 |
+
# print("Predicted '{}' is different from reference '{}'".format(t[part], ' '.join(gt[part]['words'])))
|
| 248 |
+
return False
|
| 249 |
+
return True
|
| 250 |
+
|
| 251 |
+
|
| 252 |
+
"""
|
| 253 |
+
Wire57 tuples are built like so:
|
| 254 |
+
t = {"attrib/spec?" : attrib,
|
| 255 |
+
"arg1" : {'text' : arg1, 'words': arg1_w, "words_indexes" : arg1_ind,
|
| 256 |
+
'dc_text' : arg1dc, 'decorefed_words' : arg1dc_w, 'decorefed_indexes' : arg1dc_ind},
|
| 257 |
+
"rel" : {'text' : rel, 'words': rel_w, "words_indexes" : rel_ind},
|
| 258 |
+
"arg2" : {'text' : arg2, 'words': arg2_w, "words_indexes" : arg2_ind,
|
| 259 |
+
'dc_text' : arg2dc, 'decorefed_words' : arg2dc_w, 'decorefed_indexes' : arg2dc_ind},
|
| 260 |
+
|
| 261 |
+
"""
|
| 262 |
+
|
| 263 |
+
|
| 264 |
+
def tuple_match(t, gt):
|
| 265 |
+
"""t is a predicted tuple, gt is the gold tuple. How well do they match ?
|
| 266 |
+
Yields precision and recall scores, a pair of non-zero values, if it's a match, and False if it's not.
|
| 267 |
+
"""
|
| 268 |
+
precision = [0, 0] # 0 out of 0 predicted words match
|
| 269 |
+
recall = [0, 0] # 0 out of 0 reference words match
|
| 270 |
+
# If, for each part, any word is the same as a reference word, then it's a match.
|
| 271 |
+
for part in ['arg1', 'rel', 'arg2']:
|
| 272 |
+
predicted_words = t[part].split()
|
| 273 |
+
gold_words = gt[part]['words']
|
| 274 |
+
if not predicted_words:
|
| 275 |
+
if gold_words:
|
| 276 |
+
return False
|
| 277 |
+
else:
|
| 278 |
+
continue
|
| 279 |
+
matching_words = sum(1 for w in predicted_words if w in gold_words)
|
| 280 |
+
if matching_words == 0:
|
| 281 |
+
return False # t <-> gt is not a match
|
| 282 |
+
precision[0] += matching_words
|
| 283 |
+
precision[1] += len(predicted_words)
|
| 284 |
+
# Currently this slightly penalises systems when the reference
|
| 285 |
+
# reformulates the sentence words, because the reformulation doesn't
|
| 286 |
+
# match the predicted word. It's a one-wrong-word penalty to precision,
|
| 287 |
+
# to all systems that correctly extracted the reformulated word.
|
| 288 |
+
recall[0] += matching_words
|
| 289 |
+
recall[1] += len(gold_words)
|
| 290 |
+
|
| 291 |
+
if recall[1] == 0 or precision[1] == 0:
|
| 292 |
+
return False
|
| 293 |
+
prec = precision[0] / precision[1]
|
| 294 |
+
rec = recall[0] / recall[1]
|
| 295 |
+
return [prec, rec]
|
| 296 |
+
|
| 297 |
+
|
| 298 |
+
if __name__ == "__main__":
|
| 299 |
+
parser = argparse.ArgumentParser()
|
| 300 |
+
parser.add_argument('--gold', help="file path for gold in allennlp format", required=True)
|
| 301 |
+
parser.add_argument('--system', help="file path for system in allennlp format", required=True)
|
| 302 |
+
arguments = parser.parse_args()
|
| 303 |
+
main(arguments)
|
| 304 |
+
|