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3
91
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AirAsia Zest
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Short-beaked common dolphin
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Niklas Bergqvist
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Pacific Fair Shopping Centre
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Ministry of Home Security
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The Lakes, Western Australia
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Alton, Illinois
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Us and Them (Godflesh album)
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Jonas Geirnaert
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Second River (New Jersey)
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Monson, Maine
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Thomas Wolff
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Sam Cohen (Australian politician)
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Basil Gooden
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Andrew Kiss
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Nabacalis
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The One and Only (Lil Wyte album)
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List of High Commissioners of New Zealand to Fiji
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Olen (poet)
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Michelle Ferre
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François Asselineau
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Where the Party At
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Chelsea (American band)
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Nihil
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Lélia Abramo
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William B. Maclay
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Atomic Blonde
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March for Women's Lives
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Belgian Socialist Party
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Juan Guzmán (photographer)
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Hanscarl Leuner
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Eclipse Aviation
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Calochortus lyallii
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Bartolomeo Altomonte
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Mario Amilivia

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}
}
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