{"id":13754366,"url":"https://github.com/tonytan48/Re-DocRED","last_synced_at":"2025-05-09T22:32:07.753Z","repository":{"id":37705398,"uuid":"495053419","full_name":"tonytan48/Re-DocRED","owner":"tonytan48","description":null,"archived":false,"fork":false,"pushed_at":"2023-08-21T08:15:07.000Z","size":6169,"stargazers_count":47,"open_issues_count":1,"forks_count":4,"subscribers_count":2,"default_branch":"main","last_synced_at":"2024-11-16T07:33:30.718Z","etag":null,"topics":[],"latest_commit_sha":null,"homepage":null,"language":"Python","has_issues":true,"has_wiki":null,"has_pages":null,"mirror_url":null,"source_name":null,"license":"mit","status":null,"scm":"git","pull_requests_enabled":true,"icon_url":"https://github.com/tonytan48.png","metadata":{"files":{"readme":"README.md","changelog":null,"contributing":null,"funding":null,"license":"LICENSE","code_of_conduct":null,"threat_model":null,"audit":null,"citation":null,"codeowners":null,"security":null,"support":null,"governance":null,"roadmap":null,"authors":null,"dei":null,"publiccode":null,"codemeta":null}},"created_at":"2022-05-22T12:27:09.000Z","updated_at":"2024-09-27T01:17:37.000Z","dependencies_parsed_at":"2024-08-03T09:17:22.908Z","dependency_job_id":null,"html_url":"https://github.com/tonytan48/Re-DocRED","commit_stats":null,"previous_names":[],"tags_count":0,"template":false,"template_full_name":null,"repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/tonytan48%2FRe-DocRED","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/tonytan48%2FRe-DocRED/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/tonytan48%2FRe-DocRED/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/tonytan48%2FRe-DocRED/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/tonytan48","download_url":"https://codeload.github.com/tonytan48/Re-DocRED/tar.gz/refs/heads/main","host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":253335906,"owners_count":21892756,"icon_url":"https://github.com/github.png","version":null,"created_at":"2022-05-30T11:31:42.601Z","updated_at":"2022-07-04T15:15:14.044Z","host_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub","repositories_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories","repository_names_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repository_names","owners_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners"}},"keywords":[],"created_at":"2024-08-03T09:01:56.984Z","updated_at":"2025-05-09T22:32:02.732Z","avatar_url":"https://github.com/tonytan48.png","language":"Python","funding_links":[],"categories":["关系抽取、信息抽取"],"sub_categories":["其他_文本生成、文本对话"],"readme":"# Re-DocRED Dataset\n\nThis repository contains the dataset of our EMNLP 2022 research paper [Revisiting DocRED – Addressing the False Negative Problem\nin Relation Extraction](https://arxiv.org/pdf/2205.12696.pdf). \n\nDocRED 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.\n\nThe Re-DocRED Dataset resolved the following problems of DocRED:\n1. Resolved the incompleteness problem by supplementing large amounts of relation triples.\n2. Addressed the logical inconsistencies in DocRED.\n3. Corrected the coreferential errors within DocRED.\n\n# Statistics of Re-DocRED\nThe Re-DocRED dataset is located as ./data directory, the statistics of the dataset are shown below:\n\n\n|  | Train  | Dev  |  Test  |\n| :---:   | :-: | :-: |:-: |\n| # Documents | 3,053 | 500 |  500 |\n| Avg. # Triples | 28.1 | 34.6 |  34.9 |\n| Avg. # Entities | 19.4 | 19.4 | 19.6 |\n| Avg. # Sents | 7.9 | 8.2 | 7.9 |\n\n# Citation\nIf you find our work useful, please cite our work as:\n```bibtex\n@inproceedings{tan2022revisiting,\n  title={Revisiting DocRED – Addressing the False Negative Problem in Relation Extraction},\n  author={Tan, Qingyu and Xu, Lu and Bing, Lidong and Ng, Hwee Tou and Aljunied, Sharifah Mahani},\n  booktitle={Proceedings of EMNLP},\n  url={https://arxiv.org/abs/2205.12696},\n  year={2022}\n}\n```\n\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Ftonytan48%2FRe-DocRED","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Ftonytan48%2FRe-DocRED","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Ftonytan48%2FRe-DocRED/lists"}