Datasets:

tonytan48
/

Re-DocRED

vertexSet list	labels list	sents sequence	title stringlengths 3 91
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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:

Resolved the incompleteness problem by supplementing large amounts of relation triples.
Addressed the logical inconsistencies in DocRED.
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}
}

Downloads last month: 260

Size of downloaded dataset files:

25.1 MB

Size of the auto-converted Parquet files:

4.65 MB

Number of rows:

4,053

Paper for tonytan48/Re-DocRED

Revisiting DocRED -- Addressing the False Negative Problem in Relation Extraction

Paper • 2205.12696 • Published May 25, 2022