dataset_infos.json

{"original": {"description": "TACRED is a large-scale relation extraction dataset with 106,264 examples built over newswire\n and web text from the corpus used in the yearly TAC Knowledge Base Population (TAC KBP) challenges.\n Examples in TACRED cover 41 relation types as used in the TAC KBP challenges (e.g., per:schools_attended\n and org:members) or are labeled as no_relation if no defined relation is held. These examples are created\n by combining available human annotations from the TAC KBP challenges and crowdsourcing.\n\n Please see our EMNLP paper, or our EMNLP slides for full details.\n\nNote: There is currently a label-corrected version of the TACRED dataset, which you should consider using instead of\nthe original version released in 2017. For more details on this new version, see the TACRED Revisited paper\npublished at ACL 2020.\n\nNote 2: This Datasetreader changes the offsets of the following fields, to conform with standard Python usage (see\n#_generate_examples()):\n- subj_end to subj_end + 1 (make end offset exclusive)\n- oj_end to oj_end + 1 (make end offset exclusive)\n- stanford_head to stanford_head - 1 (make head offsets 0-based)\n\nIt also changes the names of some fields:\n- token -> tokens\n- stanford_ner -> ner_tags\n- stanford_pos -> pos_tags\n", "citation": "@inproceedings{zhang-etal-2017-position,\n    title = \"Position-aware Attention and Supervised Data Improve Slot Filling\",\n    author = \"Zhang, Yuhao  and\n      Zhong, Victor  and\n      Chen, Danqi  and\n      Angeli, Gabor  and\n      Manning, Christopher D.\",\n    booktitle = \"Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing\",\n    month = sep,\n    year = \"2017\",\n    address = \"Copenhagen, Denmark\",\n    publisher = \"Association for Computational Linguistics\",\n    url = \"https://www.aclweb.org/anthology/D17-1004\",\n    doi = \"10.18653/v1/D17-1004\",\n    pages = \"35--45\",\n}\n\n@inproceedings{alt-etal-2020-tacred,\n    title = \"{TACRED} Revisited: A Thorough Evaluation of the {TACRED} Relation Extraction Task\",\n    author = \"Alt, Christoph  and\n      Gabryszak, Aleksandra  and\n      Hennig, Leonhard\",\n    booktitle = \"Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics\",\n    month = jul,\n    year = \"2020\",\n    address = \"Online\",\n    publisher = \"Association for Computational Linguistics\",\n    url = \"https://www.aclweb.org/anthology/2020.acl-main.142\",\n    doi = \"10.18653/v1/2020.acl-main.142\",\n    pages = \"1558--1569\",\n}\n", "homepage": "https://nlp.stanford.edu/projects/tacred/", "license": "LDC", "features": {"id": {"dtype": "string", "id": null, "_type": "Value"}, "docid": {"dtype": "string", 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145332199}, "revised": {"description": "TACRED is a large-scale relation extraction dataset with 106,264 examples built over newswire\n and web text from the corpus used in the yearly TAC Knowledge Base Population (TAC KBP) challenges.\n Examples in TACRED cover 41 relation types as used in the TAC KBP challenges (e.g., per:schools_attended\n and org:members) or are labeled as no_relation if no defined relation is held. These examples are created\n by combining available human annotations from the TAC KBP challenges and crowdsourcing.\n\n Please see our EMNLP paper, or our EMNLP slides for full details.\n\nNote: There is currently a label-corrected version of the TACRED dataset, which you should consider using instead of\nthe original version released in 2017. For more details on this new version, see the TACRED Revisited paper\npublished at ACL 2020.\n\nNote 2: This Datasetreader changes the offsets of the following fields, to conform with standard Python usage (see\n#_generate_examples()):\n- subj_end to subj_end + 1 (make end offset exclusive)\n- oj_end to oj_end + 1 (make end offset exclusive)\n- stanford_head to stanford_head - 1 (make head offsets 0-based)\n\nIt also changes the names of some fields:\n- token -> tokens\n- stanford_ner -> ner_tags\n- stanford_pos -> pos_tags\n", "citation": "@inproceedings{zhang-etal-2017-position,\n    title = \"Position-aware Attention and Supervised Data Improve Slot Filling\",\n    author = \"Zhang, Yuhao  and\n      Zhong, Victor  and\n      Chen, Danqi  and\n      Angeli, Gabor  and\n      Manning, Christopher D.\",\n    booktitle = \"Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing\",\n    month = sep,\n    year = \"2017\",\n    address = \"Copenhagen, 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