Datasets:
Tasks:
Text Classification
Modalities:
Text
Sub-tasks:
multi-class-classification
Languages:
English
Size:
1K - 10K
ArXiv:
| annotations_creators: | |
| - expert-generated | |
| language: | |
| - en | |
| language_creators: | |
| - found | |
| license: [] | |
| multilinguality: | |
| - monolingual | |
| pretty_name: CrossRE is a cross-domain dataset for relation extraction | |
| size_categories: | |
| - 10K<n<100K | |
| source_datasets: | |
| - extended|cross_ner | |
| tags: | |
| - cross domain | |
| - ai | |
| - news | |
| - music | |
| - literature | |
| - politics | |
| - science | |
| task_categories: | |
| - text-classification | |
| task_ids: | |
| - multi-class-classification | |
| dataset_info: | |
| - config_name: ai | |
| features: | |
| - name: doc_key | |
| dtype: string | |
| - name: sentence | |
| sequence: string | |
| - name: ner | |
| sequence: | |
| - name: id-start | |
| dtype: int32 | |
| - name: id-end | |
| dtype: int32 | |
| - name: entity-type | |
| dtype: string | |
| - name: relations | |
| sequence: | |
| - name: id_1-start | |
| dtype: int32 | |
| - name: id_1-end | |
| dtype: int32 | |
| - name: id_2-start | |
| dtype: int32 | |
| - name: id_2-end | |
| dtype: int32 | |
| - name: relation-type | |
| dtype: string | |
| - name: Exp | |
| dtype: string | |
| - name: Un | |
| dtype: bool | |
| - name: SA | |
| dtype: bool | |
| splits: | |
| - name: train | |
| num_bytes: 62411 | |
| num_examples: 100 | |
| - name: validation | |
| num_bytes: 183717 | |
| num_examples: 350 | |
| - name: test | |
| num_bytes: 217353 | |
| num_examples: 431 | |
| download_size: 508107 | |
| dataset_size: 463481 | |
| - config_name: literature | |
| features: | |
| - name: doc_key | |
| dtype: string | |
| - name: sentence | |
| sequence: string | |
| - name: ner | |
| sequence: | |
| - name: id-start | |
| dtype: int32 | |
| - name: id-end | |
| dtype: int32 | |
| - name: entity-type | |
| dtype: string | |
| - name: relations | |
| sequence: | |
| - name: id_1-start | |
| dtype: int32 | |
| - name: id_1-end | |
| dtype: int32 | |
| - name: id_2-start | |
| dtype: int32 | |
| - name: id_2-end | |
| dtype: int32 | |
| - name: relation-type | |
| dtype: string | |
| - name: Exp | |
| dtype: string | |
| - name: Un | |
| dtype: bool | |
| - name: SA | |
| dtype: bool | |
| splits: | |
| - name: train | |
| num_bytes: 62699 | |
| num_examples: 100 | |
| - name: validation | |
| num_bytes: 246214 | |
| num_examples: 400 | |
| - name: test | |
| num_bytes: 264450 | |
| num_examples: 416 | |
| download_size: 635130 | |
| dataset_size: 573363 | |
| - config_name: music | |
| features: | |
| - name: doc_key | |
| dtype: string | |
| - name: sentence | |
| sequence: string | |
| - name: ner | |
| sequence: | |
| - name: id-start | |
| dtype: int32 | |
| - name: id-end | |
| dtype: int32 | |
| - name: entity-type | |
| dtype: string | |
| - name: relations | |
| sequence: | |
| - name: id_1-start | |
| dtype: int32 | |
| - name: id_1-end | |
| dtype: int32 | |
| - name: id_2-start | |
| dtype: int32 | |
| - name: id_2-end | |
| dtype: int32 | |
| - name: relation-type | |
| dtype: string | |
| - name: Exp | |
| dtype: string | |
| - name: Un | |
| dtype: bool | |
| - name: SA | |
| dtype: bool | |
| splits: | |
| - name: train | |
| num_bytes: 69846 | |
| num_examples: 100 | |
| - name: validation | |
| num_bytes: 261497 | |
| num_examples: 350 | |
| - name: test | |
| num_bytes: 312165 | |
| num_examples: 399 | |
| download_size: 726956 | |
| dataset_size: 643508 | |
| - config_name: news | |
| features: | |
| - name: doc_key | |
| dtype: string | |
| - name: sentence | |
| sequence: string | |
| - name: ner | |
| sequence: | |
| - name: id-start | |
| dtype: int32 | |
| - name: id-end | |
| dtype: int32 | |
| - name: entity-type | |
| dtype: string | |
| - name: relations | |
| sequence: | |
| - name: id_1-start | |
| dtype: int32 | |
| - name: id_1-end | |
| dtype: int32 | |
| - name: id_2-start | |
| dtype: int32 | |
| - name: id_2-end | |
| dtype: int32 | |
| - name: relation-type | |
| dtype: string | |
| - name: Exp | |
| dtype: string | |
| - name: Un | |
| dtype: bool | |
| - name: SA | |
| dtype: bool | |
| splits: | |
| - name: train | |
| num_bytes: 49102 | |
| num_examples: 164 | |
| - name: validation | |
| num_bytes: 77952 | |
| num_examples: 350 | |
| - name: test | |
| num_bytes: 96301 | |
| num_examples: 400 | |
| download_size: 239763 | |
| dataset_size: 223355 | |
| - config_name: politics | |
| features: | |
| - name: doc_key | |
| dtype: string | |
| - name: sentence | |
| sequence: string | |
| - name: ner | |
| sequence: | |
| - name: id-start | |
| dtype: int32 | |
| - name: id-end | |
| dtype: int32 | |
| - name: entity-type | |
| dtype: string | |
| - name: relations | |
| sequence: | |
| - name: id_1-start | |
| dtype: int32 | |
| - name: id_1-end | |
| dtype: int32 | |
| - name: id_2-start | |
| dtype: int32 | |
| - name: id_2-end | |
| dtype: int32 | |
| - name: relation-type | |
| dtype: string | |
| - name: Exp | |
| dtype: string | |
| - name: Un | |
| dtype: bool | |
| - name: SA | |
| dtype: bool | |
| splits: | |
| - name: train | |
| num_bytes: 76004 | |
| num_examples: 101 | |
| - name: validation | |
| num_bytes: 277633 | |
| num_examples: 350 | |
| - name: test | |
| num_bytes: 295294 | |
| num_examples: 400 | |
| download_size: 726427 | |
| dataset_size: 648931 | |
| - config_name: science | |
| features: | |
| - name: doc_key | |
| dtype: string | |
| - name: sentence | |
| sequence: string | |
| - name: ner | |
| sequence: | |
| - name: id-start | |
| dtype: int32 | |
| - name: id-end | |
| dtype: int32 | |
| - name: entity-type | |
| dtype: string | |
| - name: relations | |
| sequence: | |
| - name: id_1-start | |
| dtype: int32 | |
| - name: id_1-end | |
| dtype: int32 | |
| - name: id_2-start | |
| dtype: int32 | |
| - name: id_2-end | |
| dtype: int32 | |
| - name: relation-type | |
| dtype: string | |
| - name: Exp | |
| dtype: string | |
| - name: Un | |
| dtype: bool | |
| - name: SA | |
| dtype: bool | |
| splits: | |
| - name: train | |
| num_bytes: 63876 | |
| num_examples: 103 | |
| - name: validation | |
| num_bytes: 224402 | |
| num_examples: 351 | |
| - name: test | |
| num_bytes: 249075 | |
| num_examples: 400 | |
| download_size: 594058 | |
| dataset_size: 537353 | |
| # Dataset Card for CrossRE | |
| ## Table of Contents | |
| - [Table of Contents](#table-of-contents) | |
| - [Dataset Description](#dataset-description) | |
| - [Dataset Summary](#dataset-summary) | |
| - [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards) | |
| - [Languages](#languages) | |
| - [Dataset Structure](#dataset-structure) | |
| - [Data Instances](#data-instances) | |
| - [Data Fields](#data-fields) | |
| - [Data Splits](#data-splits) | |
| - [Dataset Creation](#dataset-creation) | |
| - [Curation Rationale](#curation-rationale) | |
| - [Source Data](#source-data) | |
| - [Annotations](#annotations) | |
| - [Personal and Sensitive Information](#personal-and-sensitive-information) | |
| - [Considerations for Using the Data](#considerations-for-using-the-data) | |
| - [Social Impact of Dataset](#social-impact-of-dataset) | |
| - [Discussion of Biases](#discussion-of-biases) | |
| - [Other Known Limitations](#other-known-limitations) | |
| - [Additional Information](#additional-information) | |
| - [Dataset Curators](#dataset-curators) | |
| - [Licensing Information](#licensing-information) | |
| - [Citation Information](#citation-information) | |
| - [Contributions](#contributions) | |
| ## Dataset Description | |
| - **Repository:** [CrossRE](https://github.com/mainlp/CrossRE) | |
| - **Paper:** [CrossRE: A Cross-Domain Dataset for Relation Extraction](https://arxiv.org/abs/2210.09345) | |
| ### Dataset Summary | |
| CrossRE is a new, freely-available crossdomain benchmark for RE, which comprises six distinct text domains and includes | |
| multilabel annotations. It includes the following domains: news, politics, natural science, music, literature and | |
| artificial intelligence. The semantic relations are annotated on top of CrossNER (Liu et al., 2021), a cross-domain | |
| dataset for NER which contains domain-specific entity types. | |
| The dataset contains 17 relation labels for the six domains: PART-OF, PHYSICAL, USAGE, ROLE, SOCIAL, | |
| GENERAL-AFFILIATION, COMPARE, TEMPORAL, ARTIFACT, ORIGIN, TOPIC, OPPOSITE, CAUSE-EFFECT, WIN-DEFEAT, TYPEOF, NAMED, and | |
| RELATED-TO. | |
| For details, see the paper: https://arxiv.org/abs/2210.09345 | |
| ### Supported Tasks and Leaderboards | |
| [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | |
| ### Languages | |
| The language data in CrossRE is in English (BCP-47 en) | |
| ## Dataset Structure | |
| ### Data Instances | |
| #### news | |
| - **Size of downloaded dataset files:** 0.24 MB | |
| - **Size of the generated dataset:** 0.22 MB | |
| An example of 'train' looks as follows: | |
| ```python | |
| { | |
| "doc_key": "news-train-1", | |
| "sentence": ["EU", "rejects", "German", "call", "to", "boycott", "British", "lamb", "."], | |
| "ner": [ | |
| {"id-start": 0, "id-end": 0, "entity-type": "organisation"}, | |
| {"id-start": 2, "id-end": 3, "entity-type": "misc"}, | |
| {"id-start": 6, "id-end": 7, "entity-type": "misc"} | |
| ], | |
| "relations": [ | |
| {"id_1-start": 0, "id_1-end": 0, "id_2-start": 2, "id_2-end": 3, "relation-type": "opposite", "Exp": "rejects", "Un": False, "SA": False}, | |
| {"id_1-start": 2, "id_1-end": 3, "id_2-start": 6, "id_2-end": 7, "relation-type": "opposite", "Exp": "calls_for_boycot_of", "Un": False, "SA": False}, | |
| {"id_1-start": 2, "id_1-end": 3, "id_2-start": 6, "id_2-end": 7, "relation-type": "topic", "Exp": "", "Un": False, "SA": False} | |
| ] | |
| } | |
| ``` | |
| #### politics | |
| - **Size of downloaded dataset files:** 0.73 MB | |
| - **Size of the generated dataset:** 0.65 MB | |
| An example of 'train' looks as follows: | |
| ```python | |
| { | |
| "doc_key": "politics-train-1", | |
| "sentence": ["Parties", "with", "mainly", "Eurosceptic", "views", "are", "the", "ruling", "United", "Russia", ",", "and", "opposition", "parties", "the", "Communist", "Party", "of", "the", "Russian", "Federation", "and", "Liberal", "Democratic", "Party", "of", "Russia", "."], | |
| "ner": [ | |
| {"id-start": 8, "id-end": 9, "entity-type": "politicalparty"}, | |
| {"id-start": 15, "id-end": 20, "entity-type": "politicalparty"}, | |
| {"id-start": 22, "id-end": 26, "entity-type": "politicalparty"} | |
| ], | |
| "relations": [ | |
| {"id_1-start": 8, "id_1-end": 9, "id_2-start": 15, "id_2-end": 20, "relation-type": "opposite", "Exp": "in_opposition", "Un": False, "SA": False}, | |
| {"id_1-start": 8, "id_1-end": 9, "id_2-start": 22, "id_2-end": 26, "relation-type": "opposite", "Exp": "in_opposition", "Un": False, "SA": False} | |
| ] | |
| } | |
| ``` | |
| #### science | |
| - **Size of downloaded dataset files:** 0.59 MB | |
| - **Size of the generated dataset:** 0.54 MB | |
| An example of 'train' looks as follows: | |
| ```python | |
| { | |
| "doc_key": "science-train-1", | |
| "sentence": ["They", "may", "also", "use", "Adenosine", "triphosphate", ",", "Nitric", "oxide", ",", "and", "ROS", "for", "signaling", "in", "the", "same", "ways", "that", "animals", "do", "."], | |
| "ner": [ | |
| {"id-start": 4, "id-end": 5, "entity-type": "chemicalcompound"}, | |
| {"id-start": 7, "id-end": 8, "entity-type": "chemicalcompound"}, | |
| {"id-start": 11, "id-end": 11, "entity-type": "chemicalcompound"} | |
| ], | |
| "relations": [] | |
| } | |
| ``` | |
| #### music | |
| - **Size of downloaded dataset files:** 0.73 MB | |
| - **Size of the generated dataset:** 0.64 MB | |
| An example of 'train' looks as follows: | |
| ```python | |
| { | |
| "doc_key": "music-train-1", | |
| "sentence": ["In", "2003", ",", "the", "Stade", "de", "France", "was", "the", "primary", "site", "of", "the", "2003", "World", "Championships", "in", "Athletics", "."], | |
| "ner": [ | |
| {"id-start": 4, "id-end": 6, "entity-type": "location"}, | |
| {"id-start": 13, "id-end": 17, "entity-type": "event"} | |
| ], | |
| "relations": [ | |
| {"id_1-start": 13, "id_1-end": 17, "id_2-start": 4, "id_2-end": 6, "relation-type": "physical", "Exp": "", "Un": False, "SA": False} | |
| ] | |
| } | |
| ``` | |
| #### literature | |
| - **Size of downloaded dataset files:** 0.64 MB | |
| - **Size of the generated dataset:** 0.57 MB | |
| An example of 'train' looks as follows: | |
| ```python | |
| { | |
| "doc_key": "literature-train-1", | |
| "sentence": ["In", "1351", ",", "during", "the", "reign", "of", "Emperor", "Toghon", "Temür", "of", "the", "Yuan", "dynasty", ",", "93rd-generation", "descendant", "Kong", "Huan", "(", "孔浣", ")", "'", "s", "2nd", "son", "Kong", "Shao", "(", "孔昭", ")", "moved", "from", "China", "to", "Korea", "during", "the", "Goryeo", ",", "and", "was", "received", "courteously", "by", "Princess", "Noguk", "(", "the", "Mongolian-born", "wife", "of", "the", "future", "king", "Gongmin", ")", "."], | |
| "ner": [ | |
| {"id-start": 7, "id-end": 9, "entity-type": "person"}, | |
| {"id-start": 12, "id-end": 13, "entity-type": "country"}, | |
| {"id-start": 17, "id-end": 18, "entity-type": "writer"}, | |
| {"id-start": 20, "id-end": 20, "entity-type": "writer"}, | |
| {"id-start": 26, "id-end": 27, "entity-type": "writer"}, | |
| {"id-start": 29, "id-end": 29, "entity-type": "writer"}, | |
| {"id-start": 33, "id-end": 33, "entity-type": "country"}, | |
| {"id-start": 35, "id-end": 35, "entity-type": "country"}, | |
| {"id-start": 38, "id-end": 38, "entity-type": "misc"}, | |
| {"id-start": 45, "id-end": 46, "entity-type": "person"}, | |
| {"id-start": 49, "id-end": 50, "entity-type": "misc"}, | |
| {"id-start": 55, "id-end": 55, "entity-type": "person"} | |
| ], | |
| "relations": [ | |
| {"id_1-start": 7, "id_1-end": 9, "id_2-start": 12, "id_2-end": 13, "relation-type": "role", "Exp": "", "Un": False, "SA": False}, | |
| {"id_1-start": 7, "id_1-end": 9, "id_2-start": 12, "id_2-end": 13, "relation-type": "temporal", "Exp": "", "Un": False, "SA": False}, | |
| {"id_1-start": 17, "id_1-end": 18, "id_2-start": 26, "id_2-end": 27, "relation-type": "social", "Exp": "family", "Un": False, "SA": False}, | |
| {"id_1-start": 20, "id_1-end": 20, "id_2-start": 17, "id_2-end": 18, "relation-type": "named", "Exp": "", "Un": False, "SA": False}, | |
| {"id_1-start": 26, "id_1-end": 27, "id_2-start": 33, "id_2-end": 33, "relation-type": "physical", "Exp": "", "Un": False, "SA": False}, | |
| {"id_1-start": 26, "id_1-end": 27, "id_2-start": 35, "id_2-end": 35, "relation-type": "physical", "Exp": "", "Un": False, "SA": False}, | |
| {"id_1-start": 26, "id_1-end": 27, "id_2-start": 38, "id_2-end": 38, "relation-type": "temporal", "Exp": "", "Un": False, "SA": False}, | |
| {"id_1-start": 26, "id_1-end": 27, "id_2-start": 45, "id_2-end": 46, "relation-type": "social", "Exp": "greeted_by", "Un": False, "SA": False}, | |
| {"id_1-start": 29, "id_1-end": 29, "id_2-start": 26, "id_2-end": 27, "relation-type": "named", "Exp": "", "Un": False, "SA": False}, | |
| {"id_1-start": 45, "id_1-end": 46, "id_2-start": 55, "id_2-end": 55, "relation-type": "social", "Exp": "marriage", "Un": False, "SA": False}, | |
| {"id_1-start": 49, "id_1-end": 50, "id_2-start": 45, "id_2-end": 46, "relation-type": "named", "Exp": "", "Un": False, "SA": False} | |
| ] | |
| } | |
| ``` | |
| #### ai | |
| - **Size of downloaded dataset files:** 0.51 MB | |
| - **Size of the generated dataset:** 0.46 MB | |
| An example of 'train' looks as follows: | |
| ```python | |
| { | |
| "doc_key": "ai-train-1", | |
| "sentence": ["Popular", "approaches", "of", "opinion-based", "recommender", "system", "utilize", "various", "techniques", "including", "text", "mining", ",", "information", "retrieval", ",", "sentiment", "analysis", "(", "see", "also", "Multimodal", "sentiment", "analysis", ")", "and", "deep", "learning", "X.Y.", "Feng", ",", "H.", "Zhang", ",", "Y.J.", "Ren", ",", "P.H.", "Shang", ",", "Y.", "Zhu", ",", "Y.C.", "Liang", ",", "R.C.", "Guan", ",", "D.", "Xu", ",", "(", "2019", ")", ",", ",", "21", "(", "5", ")", ":", "e12957", "."], | |
| "ner": [ | |
| {"id-start": 3, "id-end": 5, "entity-type": "product"}, | |
| {"id-start": 10, "id-end": 11, "entity-type": "field"}, | |
| {"id-start": 13, "id-end": 14, "entity-type": "task"}, | |
| {"id-start": 16, "id-end": 17, "entity-type": "task"}, | |
| {"id-start": 21, "id-end": 23, "entity-type": "task"}, | |
| {"id-start": 26, "id-end": 27, "entity-type": "field"}, | |
| {"id-start": 28, "id-end": 29, "entity-type": "researcher"}, | |
| {"id-start": 31, "id-end": 32, "entity-type": "researcher"}, | |
| {"id-start": 34, "id-end": 35, "entity-type": "researcher"}, | |
| {"id-start": 37, "id-end": 38, "entity-type": "researcher"}, | |
| {"id-start": 40, "id-end": 41, "entity-type": "researcher"}, | |
| {"id-start": 43, "id-end": 44, "entity-type": "researcher"}, | |
| {"id-start": 46, "id-end": 47, "entity-type": "researcher"}, | |
| {"id-start": 49, "id-end": 50, "entity-type": "researcher"} | |
| ], | |
| "relations": [ | |
| {"id_1-start": 3, "id_1-end": 5, "id_2-start": 10, "id_2-end": 11, "relation-type": "part-of", "Exp": "", "Un": False, "SA": False}, | |
| {"id_1-start": 3, "id_1-end": 5, "id_2-start": 10, "id_2-end": 11, "relation-type": "usage", "Exp": "", "Un": False, "SA": False}, | |
| {"id_1-start": 3, "id_1-end": 5, "id_2-start": 13, "id_2-end": 14, "relation-type": "part-of", "Exp": "", "Un": False, "SA": False}, | |
| {"id_1-start": 3, "id_1-end": 5, "id_2-start": 13, "id_2-end": 14, "relation-type": "usage", "Exp": "", "Un": False, "SA": False}, | |
| {"id_1-start": 3, "id_1-end": 5, "id_2-start": 16, "id_2-end": 17, "relation-type": "part-of", "Exp": "", "Un": False, "SA": False}, | |
| {"id_1-start": 3, "id_1-end": 5, "id_2-start": 16, "id_2-end": 17, "relation-type": "usage", "Exp": "", "Un": False, "SA": False}, | |
| {"id_1-start": 3, "id_1-end": 5, "id_2-start": 26, "id_2-end": 27, "relation-type": "part-of", "Exp": "", "Un": False, "SA": False}, | |
| {"id_1-start": 3, "id_1-end": 5, "id_2-start": 26, "id_2-end": 27, "relation-type": "usage", "Exp": "", "Un": False, "SA": False}, | |
| {"id_1-start": 21, "id_1-end": 23, "id_2-start": 16, "id_2-end": 17, "relation-type": "part-of", "Exp": "", "Un": False, "SA": False}, | |
| {"id_1-start": 21, "id_1-end": 23, "id_2-start": 16, "id_2-end": 17, "relation-type": "type-of", "Exp": "", "Un": False, "SA": False} | |
| ] | |
| } | |
| ``` | |
| ### Data Fields | |
| The data fields are the same among all splits. | |
| - `doc_key`: the instance id of this sentence, a `string` feature. | |
| - `sentence`: the list of tokens of this sentence, obtained with spaCy, a `list` of `string` features. | |
| - `ner`: the list of named entities in this sentence, a `list` of `dict` features. | |
| - `id-start`: the start index of the entity, a `int` feature. | |
| - `id-end`: the end index of the entity, a `int` feature. | |
| - `entity-type`: the type of the entity, a `string` feature. | |
| - `relations`: the list of relations in this sentence, a `list` of `dict` features. | |
| - `id_1-start`: the start index of the first entity, a `int` feature. | |
| - `id_1-end`: the end index of the first entity, a `int` feature. | |
| - `id_2-start`: the start index of the second entity, a `int` feature. | |
| - `id_2-end`: the end index of the second entity, a `int` feature. | |
| - `relation-type`: the type of the relation, a `string` feature. | |
| - `Exp`: the explanation of the relation type assigned, a `string` feature. | |
| - `Un`: uncertainty of the annotator, a `bool` feature. | |
| - `SA`: existence of syntax ambiguity which poses a challenge for the annotator, a `bool` feature. | |
| ### Data Splits | |
| #### Sentences | |
| | | Train | Dev | Test | Total | | |
| |--------------|---------|---------|---------|---------| | |
| | news | 164 | 350 | 400 | 914 | | |
| | politics | 101 | 350 | 400 | 851 | | |
| | science | 103 | 351 | 400 | 854 | | |
| | music | 100 | 350 | 399 | 849 | | |
| | literature | 100 | 400 | 416 | 916 | | |
| | ai | 100 | 350 | 431 | 881 | | |
| | ------------ | ------- | ------- | ------- | ------- | | |
| | total | 668 | 2,151 | 2,46 | 5,265 | | |
| #### Relations | |
| | | Train | Dev | Test | Total | | |
| |--------------|---------|---------|---------|---------| | |
| | news | 175 | 300 | 396 | 871 | | |
| | politics | 502 | 1,616 | 1,831 | 3,949 | | |
| | science | 355 | 1,340 | 1,393 | 3,088 | | |
| | music | 496 | 1,861 | 2,333 | 4,690 | | |
| | literature | 397 | 1,539 | 1,591 | 3,527 | | |
| | ai | 350 | 1,006 | 1,127 | 2,483 | | |
| | ------------ | ------- | ------- | ------- | ------- | | |
| | total | 2,275 | 7,662 | 8,671 | 18,608 | | |
| ## Dataset Creation | |
| ### Curation Rationale | |
| [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | |
| ### Source Data | |
| #### Initial Data Collection and Normalization | |
| [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | |
| #### Who are the source language producers? | |
| [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | |
| ### Annotations | |
| #### Annotation process | |
| [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | |
| #### Who are the annotators? | |
| [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | |
| ### Personal and Sensitive Information | |
| [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | |
| ## Considerations for Using the Data | |
| ### Social Impact of Dataset | |
| [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | |
| ### Discussion of Biases | |
| [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | |
| ### Other Known Limitations | |
| [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | |
| ## Additional Information | |
| ### Dataset Curators | |
| [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | |
| ### Licensing Information | |
| [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | |
| ### Citation Information | |
| ``` | |
| @inproceedings{bassignana-plank-2022-crossre, | |
| title = "Cross{RE}: A {C}ross-{D}omain {D}ataset for {R}elation {E}xtraction", | |
| author = "Bassignana, Elisa and Plank, Barbara", | |
| booktitle = "Findings of the Association for Computational Linguistics: EMNLP 2022", | |
| year = "2022", | |
| publisher = "Association for Computational Linguistics" | |
| } | |
| ``` | |
| ### Contributions | |
| Thanks to [@phucdev](https://github.com/phucdev) for adding this dataset. |