--- license: cc-by-sa-4.0 task_categories: - text-classification - text-generation language: - he tags: - politics - parliamentary - Knesset - Hebrew - gender pretty_name: Knesset (Israeli Parliament) Proceedings Corpus size_categories: - 10M 💻 [Github Repo] • 📃 [Paper] • 📊 [ES kibana dashboard]

### Dataset Description An annotated corpus of Hebrew parliamentary proceedings containing over **35 million sentences** from all the (plenary and committee) protocols held in the Israeli parliament from 1992 to 2024. Sentences are annotated with various levels of linguistic information, including part-of-speech tags, morphological features, dependency structures, and named entities. They are also associated with detailed meta-information reflecting demographic and political properties of the speakers, based on a large database of parliament members and factions that we compiled. - **Curated by:** [Gili Goldin (University of Haifa)](https://huggingface.co./GiliGold), Nick Howell (IAHLT), Noam Ordan (IAHLT), Ella Rabinovich (The Academic College of Tel-Aviv Yaffo), Shuly Wintner (University of Haifa) For more information see: [ArXiv](https://arxiv.org/abs/2405.18115) ## Usage #### Option 1: HuggingFace For the sentences subsets such as [All Features Sentences](#all_features_sentences) subset: ```python from datasets import load_dataset subset_name = "all_features_sentences" #or "no_morph_all_features_sentences" knesset_corpus = load_dataset("HaifaCLGroup/knessetCorpus", name=subset_name, split='train', streaming=True) #streaming is recommended for example in knesset_corpus: speaker_name = example["speaker_name"] sentence_text = example["sentence_text"] gender = example["speaker_gender"] faction = example["current_faction_name"] knesset_num = int(example["knesset_number"]) print(f'knesset_number: {knesset_num}, speaker_name: {speaker_name}, sentenece_text: {sentence_text}, speaker_gender: {gender}, faction: {faction}') ``` * The [Non-Morphological Features Sentences](#non-morphological_features_sentences) subset is Ideal if morpho-syntactic annotations aren't relevant to your work, providing a less disk space heavy option. For the [Protocols](#protocols) subset: ```python from datasets import load_dataset subset_name = "protocols" knesset_corpus = load_dataset("HaifaCLGroup/knessetCorpus", name=subset_name, split='train', streaming=True) #streaming is recommended for example in knesset_corpus: protocol_name = example["protocol_name"] session_name = example["session_name"] protocol_sentences = example["protocol_sentences"] if isinstance(protocol_sentences, dict): protocol_sentences = [ {key: value[i] for key, value in protocol_sentences.items()} for i in range(len(next(iter(protocol_sentences.values())))) ] for sent in protocol_sentences: speaker_name = sent["speaker_name"] text = sent["sentence_text"] print(f'protocol: {protocol_name}, session: {session_name}, speaker: {speaker_name}, text: {text}') ``` See [Subsets](#subsets) for other subsets options and change the subset_name field accordingly. #### Option 2: Directly from files ```python import json path = #or any other sentences jsonl file with open(path, encoding="utf-8") as file: for line in file: try: sent = json.loads(line) except Exception as e: print(f'couldnt load json line. error:{e}.') sent_id = sent["sentence_id"] sent_text = sent["sentence_text"] speaker_name = sent["speaker_name"] print(f"ID: {sent_id}, speaker name: {speaker_name}, text: {sent_text") ``` #### Option 3: ElasticSearch IP address, username and password for the es server and [Kibana](http://34.0.64.248:5601/): ##### Credentials for Kibana: **_Username:_** user **_Password:_** knesset ```python elastic_ip = '34.0.64.248:9200' kibana_ip = '34.0.64.248:5601' ``` ```python es_username = 'user' es_password = 'knesset' ``` Query dataset: ```python from elasticsearch import Elasticsearch es = Elasticsearch(f'http://{elastic_ip}',http_auth=(es_username, es_password), timeout=100) resp = es.search(index="all_features_sentences", body={"query":{"match_all": {}}}) print("Got %d Hits:" % resp['hits']['total']['value']) for hit in resp['hits']['hits']: print("id: %(sentence_id)s: speaker_name: %(speaker_name)s: sentence_text: %(sentence_text)s" % hit["_source"]) ``` ## Subsets #### ALL_Features_Sentences - `name`: "all_features_sentences" - `description`: Samples of all the sentences in the corpus (plenary and committee) together with all the features available in the dataset. The fields are consistent with the [All Features Sentence](#all_features_sentence) entity. - `Number of examples`: 32,832,205 #### Non-Morphological_Features_Sentences - `name`: "no_morph_all_features_sentences" - `description`: The same as [All Features Sentences](#all_features_sentences) but without the morphological_fields features. Ideal if morpho-syntactic annotations aren't relevant to your work, providing a less disk space heavy option. - `Number of examples`: 32,832,205 #### KnessetMembers - `name`: "knessetMembers" - `description`: samples of the Knesset members in the dataset and their meta-data information such as name, gender, and factions affiliations. The fields are consistent with the [Person](#person) entity. - `Number of examples`: 1,100 #### Factions - `name`: "factions" - `description`: Samples of all the factions in the dataset and their meta-data information such as name, political orientation and active periods. The fields are consistent with the [Faction](#faction) entity. - `Number of examples`: 153 #### Protocols - `name`: "protocols" - `description`: Samples of the protocols in the dataset and their meta-data information such as date, knesset number, session name and a list of its sentences. The fields are consistent with the [Protocol](#protocol) entity. - `Number of examples`: 41,319 #### Committees_ALL_Features_Sentences - `name`: "committees_all_features_sentences" - `description`: Samples of all the sentences in the committee sessions together with all the features available in the dataset. The fields are consistent with the [All Features Sentence](#all_features_sentence) entity. - `Number of examples`: 24,805,925 #### Plenary_ALL_Features_Sentences - `name`: "plenary_all_features_sentences" - `description`: Samples of all the sentences in the plenary sessions together with all the features available in the dataset. The fields are consistent with the [All Features Sentence](#all_features_sentence) entity. - `Number of examples`: 24,805,925 #### Committees Non-Morphological_Features_Sentences - `name`: "no_morph_committee_all_features_sentences" - `description`: The same as [Committees ALL Features Sentences](#committees_aLL_features_sentences) but without the morphological_fields features. Ideal if morpho-syntactic annotations aren't relevant to your work, providing a less disk space heavy option. - `Number of examples`: 24,805,925 #### Plenary Non-Morphological_Features_Sentences - `name`: "no_morph_plenary_all_features_sentences" - `description`: The same as [Plenary_ALL_Features_Sentences](#plenary_aLL_features_sentences) but without the morphological_fields features. Ideal if morpho-syntactic annotations aren't relevant to your work, providing a less disk space heavy option. - `Number of examples`: 24,805,925 ## Other files in dataset - `ner_and_ud_manually_annotated_sentences`: contains files with ~4700 manually annotated sentences from the Knesset corpus for the NER and dependencies sentences. - `Conllu files`: The morphological fields of the sentences in a conllu format. corresponding to the morphological_fields of the [Sentence](#sentence) model. - `Meta-data files`: csv tables containing the details about the factions and the Knesset members in our dataset. corresponding the fields of the [Faction](#faction) and [Person](#person) models. - `raw_data`: All the original protocols as recieved from the Knesset in .doc, .docx and pdf formats. ## Dataset Entities and Fields ### * All the dates in the dataset are represented in the format: '%Y-%m-%d %H:%M' #### Person The Person entity contains the following fields: - `person_id`: A unique identifier for the person. For example, "2660". (type: string). - `first_name`: The first name of the person in Hebrew. For example, "אברהם". (type: string). - `last_name`: The last name of the person in Hebrew. For example, "שפירא". (type: string). - `full_name`: The full name of the person, a combination of the first and last name in Hebrew. For example, "אברהם שפירא". (type: string). - `is_knesset_member`: Indicates if the person is or ever was a Knesset member. (type: boolean). - `gender`: The person's gender. For example, "male". (type: string). - `email`: The person's email address. (type: string). - `is_current`: Indicates if the person was a Knesset member at the time this record was last updated. (type: boolean). - `last_updated_date`: The date the record was last updated. For example: "2015-03-20 12:03". (type: string). - `date_of_birth`: The person's date of birth. For example: "1921-03-02 00:00". (type: string). - `place_of_birth`: The country the person was born in, mentioned in Hebrew. For example, "רומניה". (type: string). - `year_of_aliya`: The year the person migrated to Israel if not born there. For example, "1949". Empty if the person was born in Israel or hasn't migrated there. (type: string). - `date_of_death`: The date the person died, if not alive. For example, "2000-06-26 00:00". Empty if the person is still alive. (type: string). - `mother_tongue`: The person's first language. Currently unavailable. (type: string). - `religion`: The person's religion, mentioned in Hebrew. For example "יהודי". (type: string). - `nationality`: The person's nationality, mentioned in Hebrew. For example "יהודי". (type: string). - `religious_orientation`: The person's religious orientation. Possible values:, "חרדי", "דתי", "חילוני" or an empty string if not available. (type: string). - `residence`: The place where the person currently resides. For example: "תל אביב". (type: string). - `factions_memberships`: A list of dicts that includes factions the person has been a member of. Each dict contains: - `faction_id`: General ID of the faction. (type: string). - `knesset_faction_id`: The unique ID for the faction within the Knesset. (type: string). - `faction_name`: Name of the faction in Hebrew. For example, "אגודת ישראל". (type: string). - `knesset_number`: The session of the Knesset during the person's membership in the faction. For example: "13" (type: string). - `start_date`: The date when the person's membership in the faction started. (type: string). - `end_date`: The date when the person's membership in the faction ended. (type: string). - `languages`: Languages spoken by the person, mentioned in Hebrew. For example, [" יידיש", " צרפתית", " גרמנית"] (type: list of strings). - `allSources`: The sources of information for the person, including wikiLink. (type: list of strings). - `wikiLink`: The person's Wikipedia page link. (type: string). - `notes`: Any additional notes on the person. (type: list of strings). #### Faction The Faction entity contains the following fields: - `faction_name`: Name of the faction in Hebrew. For example, "מפלגת פועלים מאוחדת". (type: string). - `faction_popular_initials`: The common initials or acronym of the party name, if any. (type: string). - `faction_id`: Unique identifier for the faction. (type: string). - `active_periods`: List of active periods for this faction, each entry is a dict: - `start_date`: The date when the active period started. (type: string). - `end_date`: The date when the active period ended. (type: string). - `knesset_numbers`: List of Knesset sessions where the faction was active. Each entry is a string representing the Knesset number. (type: list of strings). - `coalition_or_opposition_memberships`: A list of Knesset memberships, each entry is a dict that includes: - `knesset_num`: The session of the Knesset during the faction's membership. (type: string). - `start_date`: The date when the membership started. (type: string). - `end_date`: The date when the membership ended. (type: string). - `knesset_faction_name`: The faction's name in Knesset. (type: string). - `member_of_coalition`: Boolean indicating whether the faction was a member of the coalition. (type: boolean). - `notes`: Any additional notes related to the membership. (type: string). - `political_orientation`: The political orientation of the party. Possible values: "שמאל קיצוני", "שמאל", "ימין", "ימין קיצוני", "מרכז", "ערבים", "דתיים", "חרדים". (type: string). - `other_names`: Other names used to describe the faction, if any. (type: list of strings). - `notes`: Any additional notes related to the faction. (type: string). - `wiki_link`: The link to the faction's Wikipedia page in Hebrew. (type: string). #### Protocol The Protocol entity contains the following fields: - `protocol_name`: The name of the protocol document and also serves as a unique identifier for the protocol. For example, "18_ptv_140671.doc". (type: string) - `session_name`: The name of the session where the protocol was created. For example, "הוועדה לענייני ביקורת המדינה". (type: string) - `parent_session_name`: The name of the parent session where the protocol was created, if any. For example, "הוועדה לענייני ביקורת המדינה". (type: string) - `knesset_number`: The number of the Knesset session. For example, "18". (type: string) - `protocol_number`: The number of the protocol. For example, "92". (type: string) - `protocol_date`: The date and time of the meeting. For example, "2010-06-14 12:30". (type: string) - `is_ocr_output`: A Boolean value indicating whether the protocol is an output from Optical Character Recognition (OCR). Currently all documents in the dataset are not an ocr_output. (type: boolean) - `protocol_type`: The type of the protocol. possible values: "committee", "plenary". (type: string) - `protocol_sentences`: A list of sentences in the protocol. Each item in the list is a dict with fields of the [Sentence](#sentence) entity described below. #### Sentence The Sentence entity contains the following fields: - `sentence_id`: Unique identifier for the sentence. (type: string) - `protocol_name`: Name of the protocol this sentence is part of. Corresponds to the protocol_name field in the [Protocol](#protocol) entity. (type: string) - `speaker_id`: Identifier for the speaker of the sentence. Corresponds to the person_id field in the [Person](#person) entity. (type: string) - `speaker_name`: Name of the speaker. Corresponds to the full_name field in the [Person](#person) entity. (type: string) - `is_valid_speaker`: A Boolean value indicating whether the speaker is valid. (type: boolean) - `turn_num_in_protocol`: The turn number in the protocol where this sentence was spoken. (type: integer) - `sent_num_in_turn`: The number of this sentence in its respective turn. (type: integer) - `sentence_text`: The text content of the sentence. (type: string) - `is_chairman`: A Boolean value indicating whether the speaker is the chairman of this meeting. (type: boolean) - `morphological_fields`: A List of morphological structures of words in the sentence, each being a dictionary. These fields are based on the CoNLL-U morphological annotations format: - `id`: The identifier for the word in the sentence. (type: integer) - `form`: The form of the word. (type: string) - `lemma`: The base or dictionary form of the word. (type: string) - `upos`: Universal part-of-speech tag. (type: string) - `xpos`: Language-specific part-of-speech tag. (type: string) - `feats`: Grammatical features of the word. This is a dictionary with features such as: {"Gender": "Masc", "Number": "Plur"}. (type: dictionary) - `head`: The ID of the word that the current word is attached to, creating a syntactic relation. (type: integer) - `deprel`: Universal dependency relation to the HEAD (root independent). (type: string) - `deps`: Enhanced dependency graph in the form of a list of head-deprel pairs. (type: list) - `misc`: Any other miscellaneous information. (type: string) - `factuality_fields`: Currently unavailable. #### All_Features_Sentence The All_Features_Sentence entity combines fields from the [Person](#person),[Faction](#faction), [Protocol](#protocol) and [Sentence](#sentence) entities, each corresponding to its specific context in relation to the sentence. This is roughly equivalent to a join between all the entities in dataset. Each field corresponds to its respective description in the entity's section. The structure includes the following fields: - Protocol fields: These correspond to the protocol from which the sentence is extracted and include: `knesset_number`, `protocol_name`, `protocol_number`, `protocol_type`, `session_name`, `parent_session_name`, `protocol_date`, `is_ocr_output`. - Sentence fields: These correspond to the specific sentence and include: `sentence_id`, `speaker_id`, `speaker_name`, `is_valid_speaker`, `is_chairman`, `turn_num_in_protocol`, `sent_num_in_turn`, `sentence_text`, `morphological_fields`, `factuality_fields`. - Person (Speaker) fields: These correspond to the speaker of the sentence and include: `speaker_first_name`, `speaker_last_name`, `speaker_is_knesset_member`, `speaker_gender`, `speaker_email`, `speaker_last_updated_date`, `speaker_date_of_birth`, `speaker_place_of_birth`,`speaker_year_of_aliya`, `speaker_date_of_death`, `speaker_mother_tongue`, `speaker_religion`, `speaker_nationality`, `speaker_religious_orientation`, `speaker_residence`, `speaker_factions_memberships`, `speaker_languages`, `speaker_sources`, `speaker_notes`. - Faction fields: These correspond to the faction of the speaker at the time the sentence was delivered and include: `faction_id`, `faction_general_name`, `knesset_faction_id`, `current_faction_name`, `member_of_coalition_or_opposition`, `faction_popular_initials`, `faction_active_periods`, `faction_knesset_numbers`,`faction_coalition_or_opposition_memberships`, `faction_political_orientation`, `faction_other_names`, `faction_notes`, `faction_wiki_link`. Please refer to the respective entity section for details on each field. ### License license: cc-by-sa-4.0 The raw data files were received from the [Knesset archives](https://main.knesset.gov.il/Pages/default.aspx). The original data are copyright-free and are released under no license. [![CC BY SA 4.0][cc-by-shield]][cc-by] This work is licensed under a [Creative Commons Attribution 4.0 International License][cc-by]. [cc-by]: https://creativecommons.org/licenses/by-sa/4.0/ [cc-by-shield]: https://licensebuttons.net/l/by-sa/4.0/80x15.png ### Citation If you use this dataset in your research, please cite the following paper: ``` @article{Goldin2024TheKC, title={The {K}nesset {C}orpus: An Annotated Corpus of {H}ebrew Parliamentary Proceedings}, author={Gili Goldin and Nick Howell and Noam Ordan and Ella Rabinovich and Shuly Wintner}, journal={ArXiv}, year={2024}, volume={abs/2405.18115}, url={https://api.semanticscholar.org/CorpusID:270068168} } ```