데이터셋 탐색

TACRED is a large-scale relation extraction dataset with 106,264 examples built over newswire and web text from the corpus used in the yearly TAC Knowledge Base Population (TAC KBP) challenges. Examples in TACRED cover 41 relation types as used in the TAC KBP challenges (e.g., per:schools_attended and org:members) or are labeled as no_relation if no defined relation is held. These examples are created by combining available human annotations from the TAC KBP challenges and crowdsourcing. Source: https://nlp.stanford.edu/projects/tacred/

The Re-TACRED dataset is a significantly improved version of the TACRED dataset for relation extraction. Using new crowd-sourced labels, Re-TACRED prunes poorly annotated sentences and addresses TACRED relation definition ambiguity, ultimately correcting 23.9% of TACRED labels. This dataset contains over 91 thousand sentences spread across 40 relations. Dataset presented at AAAI 2021. Paper (arXiv): https://arxiv.org/abs/2104.08398

The TACRED-Revisited dataset improves the crowd-sourced TACRED dataset for relation extraction by relabeling the dev and test sets using expert linguistic annotators. Relabeling focuses on the 5K most challenging instances in dev and test, in total, 51.2% of these are corrected. Published at ACL 2020. Paper (arXiv): https://arxiv.org/abs/2004.14855

MultiTACRED is a multilingual version of the large-scale TAC Relation Extraction Dataset. It covers 12 typologically diverse languages from 9 language families, and was created by the Speech & Language Technology group of DFKI by machine-translating the instances of the original TACRED dataset and automatically projecting their entity annotations. For details of the original TACRED's data collection and annotation process, see the Stanford paper. Translations are syntactically validated by checking the correctness of the XML tag markup. Any translations with an invalid tag structure, e.g. missing or invalid head or tail tag pairs, are discarded (on average, 2.3% of the instances). Languages covered are: Arabic, Chinese, Finnish, French, German, Hindi, Hungarian, Japanese, Polish, Russian, Spanish, Turkish. Intended use is supervised relation classification. Audience - researchers. MultiTACRED is released via the Linguistic Data Consortium (LDC License). You can download MultiTACRED from the LDC MultiTACRED webpage. Please see our ACL paper for full details.

533 parallel examples sampled from TACRED, translated into Russian and Korean (and 3 additional examples in Russian), accompanied with tranlsation of a list of trigger words collected for the different relations.