README.md

metadata

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
• Dataset Description
  • Dataset Summary
  • Supported Tasks and Leaderboards
  • Languages
• Dataset Structure
  • Data Instances
  • Data Fields
  • Data Splits
• Dataset Creation
  • Curation Rationale
  • Source Data
  • Annotations
  • Personal and Sensitive Information
• Considerations for Using the Data
  • Social Impact of Dataset
  • Discussion of Biases
  • Other Known Limitations
• Additional Information
  • Dataset Curators
  • Licensing Information
  • Citation Information
  • Contributions

Dataset Description

• Repository: CrossRE
• Paper: CrossRE: A Cross-Domain Dataset for Relation Extraction

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

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:

{
  "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:

{
  "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:

{
  "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:

{
  "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:

{
  "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:

{
  "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

Source Data

Initial Data Collection and Normalization

More Information Needed

Who are the source language producers?

More Information Needed

Annotations

Annotation process

More Information Needed

Who are the annotators?

More Information Needed

Personal and Sensitive Information

More Information Needed

Considerations for Using the Data

Social Impact of Dataset

More Information Needed

Discussion of Biases

More Information Needed

Other Known Limitations

More Information Needed

Additional Information

Dataset Curators

More Information Needed

Licensing Information

More Information Needed

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 for adding this dataset.