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Re-DocRED Dataset

This repository contains the dataset of our EMNLP 2022 research paper Revisiting DocRED – Addressing the False Negative Problem in Relation Extraction.

DocRED is a widely used benchmark for document-level relation extraction. However, the DocRED dataset contains a significant percentage of false negative examples (incomplete annotation). We revised 4,053 documents in the DocRED dataset and resolved its problems. We released this dataset as: Re-DocRED dataset.

The Re-DocRED Dataset resolved the following problems of DocRED:

  1. Resolved the incompleteness problem by supplementing large amounts of relation triples.
  2. Addressed the logical inconsistencies in DocRED.
  3. Corrected the coreferential errors within DocRED.

Statistics of Re-DocRED

The Re-DocRED dataset is located as ./data directory, the statistics of the dataset are shown below:

Train Dev Test
# Documents 3,053 500 500
Avg. # Triples 28.1 34.6 34.9
Avg. # Entities 19.4 19.4 19.6
Avg. # Sents 7.9 8.2 7.9

Citation

If you find our work useful, please cite our work as:

@inproceedings{tan2022revisiting,
  title={Revisiting DocRED – Addressing the False Negative Problem in Relation Extraction},
  author={Tan, Qingyu and Xu, Lu and Bing, Lidong and Ng, Hwee Tou and Aljunied, Sharifah Mahani},
  booktitle={Proceedings of EMNLP},
  url={https://arxiv.org/abs/2205.12696},
  year={2022}
}