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COPYRIGHT DELETED
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- Experiment resources related to the MuLMS corpus.
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- Copyright (c) 2023 Robert Bosch GmbH
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-
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- This program is free software: you can redistribute it and/or modify
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- it under the terms of the GNU Affero General Public License as published
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- by the Free Software Foundation, either version 3 of the License, or
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- (at your option) any later version.
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-
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- This program is distributed in the hope that it will be useful,
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- but WITHOUT ANY WARRANTY; without even the implied warranty of
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- MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
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- GNU Affero General Public License for more details.
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- You should have received a copy of the GNU Affero General Public License
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- along with this program. If not, see <https://www.gnu.org/licenses/>.
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
LICENSE_CORPUS DELETED
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README.md CHANGED
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  ---
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  license: cc-by-sa-4.0
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  language:
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- - en
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  size_categories:
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- - 10K<n<100K
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  source_datasets:
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- - original
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  task_categories:
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- - fill-mask
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- - token-classification
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- - text-classification
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  task_ids:
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- - named-entity-recognition
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- - slot-filling
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  pretty_name: Multi-Layer Materials Science Corpus
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  configs:
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- - config_name: MuLMS_Corpus
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- data_files:
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- - split: train
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- path: MuLMS_Corpus/train-*
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- - split: validation
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- - split: test
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- - config_name: MuLMS_Corpus
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- features:
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- - name: doc_id
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- dtype: string
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- - name: sentence
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- dtype: string
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- - name: tokens
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- - name: ID
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- - name: validation
113
- num_bytes: 9320669
114
- num_examples: 42010
115
- - name: test
116
- num_bytes: 8450774
117
- num_examples: 33921
118
- download_size: 3139932
119
- dataset_size: 68288938
120
  ---
121
 
122
  # Dataset Card for MuLMS
@@ -128,17 +128,17 @@ dataset_info:
128
 
129
  ### Dataset Description
130
 
131
- The Multi-Layer Materials Science corpus (MuLMS) consists of 50 documents (licensed CC BY) from the materials science domain, spanning across the following 7 subareas:
132
- "Electrolysis", "Graphene", "Polymer Electrolyte Fuel Cell (PEMFC)", "Solid Oxide Fuel Cell (SOFC)", "Polymers", "Semiconductors" and "Steel".
133
  It was exhaustively annotated by domain experts. There are annotations on sentence-level and token-level for the following NLP tasks:
134
 
135
- - **Measurement Frames**: Measurement annotations are treated in a frame-like fashion, using the span type MEASUREMENT to mark the triggers (e.g.,
136
- was measured, is plotted) that introduce the Measurement frame to the discourse. Deciding whether a sentence contains a measurement trigger is treated as a
137
- sentence-level task, determining the span that triggers the measurement frame is treated as named entity recognition.
138
- - **Named Entities**: There are 12 token-level named entities (+ Measurement trigger) available in MuLMS. Named entities can span across multiple tokens.
139
- - **Relations**: MuLMS provides relations between pairs of entities. There are two types of relations: measurement-related relations and further relations.
140
- The first type always starts at Measurement trigger spans, the scond type does not start at a specific Measurement annotation.
141
- - **Argumentative Zones**: Each sentence in MuLMS is assigned a rhetorical function in the discourse (e.g., _Background_ or _Experiment_Preparation_). There are 12 argumentative
142
  zones in MuLMS, which leads to a sentence-level classification task.
143
 
144
  You can find all experiment code files and further information in the [MuLMS-AZ Repo](https://github.com/boschresearch/mulms-az-codi2023) and [MuLMS Repo](https://github.com/boschresearch/mulms-wiesp2023).
@@ -153,6 +153,8 @@ For dataset statistics, please refer to both papers listed below. There you can
153
 
154
  MuLMS provides all annotated files in UIMA CAS XMI format that can be used with annotation tools that can read these files such as [INCEpTION](https://inception-project.github.io/).
155
 
 
 
156
  ### Dataset Sources
157
 
158
  <!-- Provide the basic links for the dataset. -->
@@ -209,7 +211,7 @@ HuggingFace datasets do not support these sub-splits, hence they must be loaded
209
 
210
  Each instance in this config refers to one token and carries a copy of the entire sentence, i.e., for _n_ tokens in a sentence, the text of the sentence is given _n_ times.
211
 
212
- - `index`: Unique instance ID for each token.
213
  - `ID`: Sentence ID. As opposed to the other config, the sentences here are not sorted by document and provided in their full form for every token they belong to.
214
  - `Sentence`: Sentence string
215
  - `Token_ID`: Unique ID for each token within each sentence. ID is resetted for each new sentence.
@@ -222,52 +224,52 @@ Each instance in this config refers to one token and carries a copy of the entir
222
 
223
  For the different layers, the following labels are available:
224
 
225
- - **Measurement Frames**:
226
- - `Measurement`
227
- - `Qual_Measurement`
228
- - **Named Entities**:
229
- - `MAT`
230
- - `NUM`
231
- - `VALUE`
232
- - `UNIT`
233
- - `PROPERTY`
234
- - `FORM`
235
- - `MEASUREMENT` (measurement frame-evoking trigger)
236
- - `CITE`
237
- - `SAMPLE`
238
- - `TECHNIQUE`
239
- - `DEV`
240
- - `RANGE`
241
- - `INSTRUMENT`
242
- - **Relations**:
243
- - `hasForm`
244
- - `measuresProperty`
245
- - `usedAs`
246
- - `propertyValue`
247
- - `conditionProperty`
248
- - `conditionSample`
249
- - `conditionPropertyValue`
250
- - `usesTechnique`
251
- - `measuresPropertyValue`
252
- - `usedTogether`
253
- - `conditionEnv`
254
- - `usedIn`
255
- - `conditionInstrument`
256
- - `takenFrom`
257
- - `dopedBy`
258
- - **Argumentative Zones**:
259
- - `Motivation`
260
- - `Background`
261
- - `PriorWork`
262
- - `Experiment`
263
- - `Preparation`
264
- - `Characterization`
265
- - `Explanation`
266
- - `Results`
267
- - `Conclusion`
268
- - `Heading`
269
- - `Caption`
270
- - `Metadata`
271
 
272
  ## Dataset Creation
273
 
@@ -293,7 +295,7 @@ You can find all the authors for every document in this corpus in [MuLMS_Corpus_
293
 
294
  #### Annotation process
295
 
296
- The annotation process included guideline design in dedicated discussion sessions. Afterward, the text files were annotated
297
  using [INCEpTION](https://inception-project.github.io/).
298
 
299
  #### Who are the annotators?
@@ -313,10 +315,9 @@ This dataset does not contain any personal, sensitive or private data. MuLMS bui
313
  If you use our software or dataset in your scientific work, please cite both papers:
314
 
315
  **BibTeX:**
316
-
317
  ```
318
  @misc{schrader2023mulms,
319
- title={MuLMS: A Multi-Layer Annotated Text Corpus for Information Extraction in the Materials Science Domain},
320
  author={Timo Pierre Schrader and Matteo Finco and Stefan Grünewald and Felix Hildebrand and Annemarie Friedrich},
321
  year={2023},
322
  eprint={2310.15569},
@@ -377,12 +378,12 @@ The MuLMS corpus is released under the [CC BY-SA 4.0](https://creativecommons.or
377
 
378
  ## Dataset Card Authors
379
 
380
- - Timo Pierre Schrader (Bosch Center for AI, University of Augsburg)
381
- - Matteo Finco (Bosch Research)
382
- - Stefan Grünewald (Bosch Center for AI, University of Stuttgart)
383
- - Felix Hildebrand (Bosch Research)
384
- - Annemarie Friedrich (University of Augsburg)
385
 
386
  ## Dataset Card Contact
387
 
388
- For all questions, please contact [Timo Schrader](mailto:[email protected]).
 
1
  ---
2
  license: cc-by-sa-4.0
3
  language:
4
+ - en
5
  size_categories:
6
+ - 10K<n<100K
7
  source_datasets:
8
+ - original
9
  task_categories:
10
+ - fill-mask
11
+ - token-classification
12
+ - text-classification
13
  task_ids:
14
+ - named-entity-recognition
15
+ - slot-filling
16
  pretty_name: Multi-Layer Materials Science Corpus
17
  configs:
18
+ - config_name: MuLMS_Corpus
19
+ data_files:
20
+ - split: train
21
+ path: MuLMS_Corpus/train-*
22
+ - split: validation
23
+ path: MuLMS_Corpus/validation-*
24
+ - split: test
25
+ path: MuLMS_Corpus/test-*
26
+ default: true
27
+ - config_name: NER_Dependencies
28
+ data_files:
29
+ - split: train
30
+ path: NER_Dependencies/train-*
31
+ - split: validation
32
+ path: NER_Dependencies/validation-*
33
+ - split: test
34
+ path: NER_Dependencies/test-*
35
  dataset_info:
36
+ - config_name: MuLMS_Corpus
37
+ features:
38
+ - name: doc_id
39
+ dtype: string
40
+ - name: sentence
41
+ dtype: string
42
+ - name: tokens
43
+ sequence: string
44
+ - name: beginOffset
45
+ dtype: int32
46
+ - name: endOffset
47
+ dtype: int32
48
+ - name: AZ_labels
49
+ dtype: string
50
+ - name: Measurement_label
51
+ dtype: string
52
+ - name: NER_labels
53
+ sequence:
54
+ - name: text
55
+ dtype: string
56
+ - name: id
57
+ dtype: int32
58
+ - name: value
59
+ dtype: string
60
+ - name: begin
61
+ dtype: string
62
+ - name: end
63
+ dtype: string
64
+ - name: tokenIndices
65
+ sequence: int32
66
+ - name: NER_labels_BILOU
67
+ sequence: string
68
+ - name: relations
69
+ sequence:
70
+ - name: ne_id_gov
71
+ dtype: int32
72
+ - name: ne_id_dep
73
+ dtype: int32
74
+ - name: label
75
+ dtype: string
76
+ - name: docFileName
77
+ dtype: string
78
+ - name: data_split
79
+ dtype: string
80
+ - name: category
81
+ dtype: string
82
+ splits:
83
+ - name: train
84
+ num_bytes: 7319898
85
+ num_examples: 7538
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+ - name: validation
87
+ num_bytes: 1499121
88
+ num_examples: 1532
89
+ - name: test
90
+ num_bytes: 1236358
91
+ num_examples: 1114
92
+ download_size: 2792635
93
+ dataset_size: 10055377
94
+ - config_name: NER_Dependencies
95
+ features:
96
+ - name: ID
97
+ dtype: int32
98
+ - name: sentence
99
+ dtype: string
100
+ - name: token_id
101
+ dtype: int32
102
+ - name: token_text
103
+ dtype: string
104
+ - name: NE_Dependencies
105
+ dtype: string
106
+ - name: data_split
107
+ dtype: string
108
+ splits:
109
+ - name: train
110
+ num_bytes: 50517495
111
+ num_examples: 216806
112
+ - name: validation
113
+ num_bytes: 9320669
114
+ num_examples: 42010
115
+ - name: test
116
+ num_bytes: 8450774
117
+ num_examples: 33921
118
+ download_size: 3139932
119
+ dataset_size: 68288938
120
  ---
121
 
122
  # Dataset Card for MuLMS
 
128
 
129
  ### Dataset Description
130
 
131
+ The Multi-Layer Materials Science corpus (MuLMS) consists of 50 documents (licensed CC BY) from the materials science domain, spanning across the following 7 subareas:
132
+ "Electrolysis", "Graphene", "Polymer Electrolyte Fuel Cell (PEMFC)", "Solid Oxide Fuel Cell (SOFC)", "Polymers", "Semiconductors" and "Steel".
133
  It was exhaustively annotated by domain experts. There are annotations on sentence-level and token-level for the following NLP tasks:
134
 
135
+ * __Measurement Frames__: Measurement annotations are treated in a frame-like fashion, using the span type MEASUREMENT to mark the triggers (e.g.,
136
+ was measured, is plotted) that introduce the Measurement frame to the discourse. Deciding whether a sentence contains a measurement trigger is treated as a
137
+ sentence-level task, determining the span that triggers the measurement frame is treated as named entity recognition.
138
+ * __Named Entities__: There are 12 token-level named entities (+ Measurement trigger) available in MuLMS. Named entities can span across multiple tokens.
139
+ * __Relations__: MuLMS provides relations between pairs of entities. There are two types of relations: measurement-related relations and further relations.
140
+ The first type always starts at Measurement trigger spans, the scond type does not start at a specific Measurement annotation.
141
+ * __Argumentative Zones__: Each sentence in MuLMS is assigned a rhetorical function in the discourse (e.g., _Background_ or _Experiment_Preparation_). There are 12 argumentative
142
  zones in MuLMS, which leads to a sentence-level classification task.
143
 
144
  You can find all experiment code files and further information in the [MuLMS-AZ Repo](https://github.com/boschresearch/mulms-az-codi2023) and [MuLMS Repo](https://github.com/boschresearch/mulms-wiesp2023).
 
153
 
154
  MuLMS provides all annotated files in UIMA CAS XMI format that can be used with annotation tools that can read these files such as [INCEpTION](https://inception-project.github.io/).
155
 
156
+ __Important:__ To use the dataset reader, please install the UIMA CAS Python reader _puima_ using the following command: `pip install git+https://github.com/annefried/puima.git`.
157
+
158
  ### Dataset Sources
159
 
160
  <!-- Provide the basic links for the dataset. -->
 
211
 
212
  Each instance in this config refers to one token and carries a copy of the entire sentence, i.e., for _n_ tokens in a sentence, the text of the sentence is given _n_ times.
213
 
214
+ - `index`: Unique instance ID for each token.
215
  - `ID`: Sentence ID. As opposed to the other config, the sentences here are not sorted by document and provided in their full form for every token they belong to.
216
  - `Sentence`: Sentence string
217
  - `Token_ID`: Unique ID for each token within each sentence. ID is resetted for each new sentence.
 
224
 
225
  For the different layers, the following labels are available:
226
 
227
+ * __Measurement Frames__:
228
+ * `Measurement`
229
+ * `Qual_Measurement`
230
+ * __Named Entities__:
231
+ * `MAT`
232
+ * `NUM`
233
+ * `VALUE`
234
+ * `UNIT`
235
+ * `PROPERTY`
236
+ * `FORM`
237
+ * `MEASUREMENT` (measurement frame-evoking trigger)
238
+ * `CITE`
239
+ * `SAMPLE`
240
+ * `TECHNIQUE`
241
+ * `DEV`
242
+ * `RANGE`
243
+ * `INSTRUMENT`
244
+ * __Relations__:
245
+ * `hasForm`
246
+ * `measuresProperty`
247
+ * `usedAs`
248
+ * `propertyValue`
249
+ * `conditionProperty`
250
+ * `conditionSample`
251
+ * `conditionPropertyValue`
252
+ * `usesTechnique`
253
+ * `measuresPropertyValue`
254
+ * `usedTogether`
255
+ * `conditionEnv`
256
+ * `usedIn`
257
+ * `conditionInstrument`
258
+ * `takenFrom`
259
+ * `dopedBy`
260
+ * __Argumentative Zones__:
261
+ * `Motivation`
262
+ * `Background`
263
+ * `PriorWork`
264
+ * `Experiment`
265
+ * `Preparation`
266
+ * `Characterization`
267
+ * `Explanation`
268
+ * `Results`
269
+ * `Conclusion`
270
+ * `Heading`
271
+ * `Caption`
272
+ * `Metadata`
273
 
274
  ## Dataset Creation
275
 
 
295
 
296
  #### Annotation process
297
 
298
+ The annotation process included guideline design in dedicated discussion sessions. Afterward, the text files were annotated
299
  using [INCEpTION](https://inception-project.github.io/).
300
 
301
  #### Who are the annotators?
 
315
  If you use our software or dataset in your scientific work, please cite both papers:
316
 
317
  **BibTeX:**
 
318
  ```
319
  @misc{schrader2023mulms,
320
+ title={MuLMS: A Multi-Layer Annotated Text Corpus for Information Extraction in the Materials Science Domain},
321
  author={Timo Pierre Schrader and Matteo Finco and Stefan Grünewald and Felix Hildebrand and Annemarie Friedrich},
322
  year={2023},
323
  eprint={2310.15569},
 
378
 
379
  ## Dataset Card Authors
380
 
381
+ * Timo Pierre Schrader (Bosch Center for AI, University of Augsburg)
382
+ * Matteo Finco (Bosch Research)
383
+ * Stefan Grünewald (Bosch Center for AI, University of Stuttgart)
384
+ * Felix Hildebrand (Bosch Research)
385
+ * Annemarie Friedrich (University of Augsburg)
386
 
387
  ## Dataset Card Contact
388
 
389
+ For all questions, please contact [Timo Schrader](mailto:[email protected]).
teaser.png DELETED

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