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license: mit |
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# Re-DocRED Dataset |
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This repository contains the dataset of our EMNLP 2022 research paper [Revisiting DocRED – Addressing the False Negative Problem |
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in Relation Extraction](https://arxiv.org/pdf/2205.12696.pdf). |
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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. |
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The Re-DocRED Dataset resolved the following problems of DocRED: |
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1. Resolved the incompleteness problem by supplementing large amounts of relation triples. |
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2. Addressed the logical inconsistencies in DocRED. |
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3. Corrected the coreferential errors within DocRED. |
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# Statistics of Re-DocRED |
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The Re-DocRED dataset is located as ./data directory, the statistics of the dataset are shown below: |
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| | Train | Dev | Test | |
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| :---: | :-: | :-: |:-: | |
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| # Documents | 3,053 | 500 | 500 | |
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| Avg. # Triples | 28.1 | 34.6 | 34.9 | |
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| Avg. # Entities | 19.4 | 19.4 | 19.6 | |
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| Avg. # Sents | 7.9 | 8.2 | 7.9 | |
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# Citation |
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If you find our work useful, please cite our work as: |
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```bibtex |
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@inproceedings{tan2022revisiting, |
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title={Revisiting DocRED – Addressing the False Negative Problem in Relation Extraction}, |
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author={Tan, Qingyu and Xu, Lu and Bing, Lidong and Ng, Hwee Tou and Aljunied, Sharifah Mahani}, |
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booktitle={Proceedings of EMNLP}, |
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url={https://arxiv.org/abs/2205.12696}, |
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year={2022} |
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} |
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``` |
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