Joint entity and relation extraction (JERE) is one of the most important tasks in information extraction. However, most existing works focus on sentence-level coarse-grained JERE, which have limitations in real-world scenarios. In this paper, we construct a large-scale document-level fine-grained JERE dataset DocRED-FE, which improves DocRED with Fine-Grained Entity Type. Specifically, we redesign a hierarchical entity type schema including 11 coarse-grained types and 119 fine-grained types, and then re-annotate DocRED manually according to this schema. Through comprehensive experiments we find that: (1) DocRED-FE is challenging to existing JERE models; (2) Our fine-grained entity types promote relation classification. We make DocRED-FE with instruction and the code for our baselines publicly available at https://github.com/PKU-TANGENT/DOCRED-FE.
@article{arxiv.2303.11141,
title = {DocRED-FE: A Document-Level Fine-Grained Entity And Relation Extraction Dataset},
author = {Hongbo Wang and Weimin Xiong and Yifan Song and Dawei Zhu and Yu Xia and Sujian Li},
journal= {arXiv preprint arXiv:2303.11141},
year = {2023}
}
Comments
Accepted by IEEE ICASSP 2023. The first two authors contribute equally