Dataset Card for "docred"

Dataset Description
Dataset Structure
Dataset Creation
Considerations for Using the Data
Additional Information

Dataset Description

Homepage: https://github.com/thunlp/DocRED
Repository: More Information Needed
Paper: More Information Needed
Point of Contact: More Information Needed
Size of downloaded dataset files: 20.03 MB
Size of the generated dataset: 19.19 MB
Total amount of disk used: 39.23 MB

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.

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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.

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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 Sample Size

name	train_annotated	train_distant	validation	test
default	3053	1000	1000	1000

@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}
}

Dataset Card for "docred"

Table of Contents

Dataset Description

Dataset Summary

Supported Tasks

Languages

Dataset Structure

Data Instances

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Data Fields

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Data Splits Sample Size

Dataset Creation

Curation Rationale

Source Data

Annotations

Personal and Sensitive Information

Considerations for Using the Data

Discussion of Biases

Other Known Limitations

Additional Information

Dataset Curators

Licensing Information

Citation Information

Models trained or fine-tuned on docred

nielsr/coref-bert-base

nielsr/coref-bert-large

nielsr/coref-roberta-base

nielsr/coref-roberta-large

Dataset Card for "docred"

Table of Contents

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How to load this dataset directly with the datasets library:

Models trained or fine-tuned on docred