Datasets:
docred

Task Categories: text-retrieval
Languages: en
Multilinguality: monolingual
Size Categories: 100K<n<1M
Licenses: mit
Language Creators: crowdsourced
Annotations Creators: expert-generated
Source Datasets: original

Dataset Card for DocRED

Dataset Summary

Multiple entities in a document generally exhibit complex inter-sentence relations, and cannot be well handled by existing relation extraction (RE) methods that typically focus on extracting intra-sentence relations for single entity pairs. In order to accelerate the research on document-level RE, we introduce DocRED, a new dataset constructed from Wikipedia and Wikidata with three features: - DocRED annotates both named entities and relations, and is the largest human-annotated dataset for document-level RE from plain text. - DocRED requires reading multiple sentences in a document to extract entities and infer their relations by synthesizing all information of the document. - Along with the human-annotated data, we also offer large-scale distantly supervised data, which enables DocRED to be adopted for both supervised and weakly supervised scenarios.

Supported Tasks and Leaderboards

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Languages

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Dataset Structure

We show detailed information for up to 5 configurations of the dataset.

Data Instances

default

  • Size of downloaded dataset files: 20.03 MB
  • Size of the generated dataset: 19.19 MB
  • Total amount of disk used: 39.23 MB

An example of 'train_annotated' looks as follows.

{
    "labels": {
        "evidence": [[0]],
        "head": [0],
        "relation_id": ["P1"],
        "relation_text": ["is_a"],
        "tail": [0]
    },
    "sents": [["This", "is", "a", "sentence"], ["This", "is", "another", "sentence"]],
    "title": "Title of the document",
    "vertexSet": [[{
        "name": "sentence",
        "pos": [3],
        "sent_id": 0,
        "type": "NN"
    }, {
        "name": "sentence",
        "pos": [3],
        "sent_id": 1,
        "type": "NN"
    }], [{
        "name": "This",
        "pos": [0],
        "sent_id": 0,
        "type": "NN"
    }]]
}

Data Fields

The data fields are the same among all splits.

default

  • title: a string feature.
  • sents: a dictionary feature containing:
    • feature: a string feature.
  • name: a string feature.
  • sent_id: a int32 feature.
  • pos: a list of int32 features.
  • type: a string feature.
  • labels: a dictionary feature containing:
    • head: a int32 feature.
    • tail: a int32 feature.
    • relation_id: a string feature.
    • relation_text: a string feature.
    • evidence: a list of int32 features.

Data Splits

name train_annotated train_distant validation test
default 3053 101873 998 1000

Dataset Creation

Curation Rationale

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Source Data

Initial Data Collection and Normalization

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Who are the source language producers?

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Annotations

Annotation process

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Who are the annotators?

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Personal and Sensitive Information

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Considerations for Using the Data

Social Impact of Dataset

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Discussion of Biases

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Other Known Limitations

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Additional Information

Dataset Curators

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Licensing Information

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Citation Information

@inproceedings{yao2019DocRED,
  title={{DocRED}: A Large-Scale Document-Level Relation Extraction Dataset},
  author={Yao, Yuan and Ye, Deming and Li, Peng and Han, Xu and Lin, Yankai and Liu, Zhenghao and Liu,   Zhiyuan and Huang, Lixin and Zhou, Jie and Sun, Maosong},
  booktitle={Proceedings of ACL 2019},
  year={2019}
}

Contributions

Thanks to @ghomasHudson, @thomwolf, @lhoestq for adding this dataset.

Models trained or fine-tuned on docred