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README.md

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 license: mit
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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](https://arxiv.org/pdf/2205.12696.pdf).
+
+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:
+```bibtex
+@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}
+}
+```
+