A Span-Based Model for Joint Overlapped and Discontinuous Named Entity Recognition
Fei Li, ZhiChao Lin, Meishan Zhang, Donghong Ji
Abstract
Research on overlapped and discontinuous named entity recognition (NER) has received increasing attention. The majority of previous work focuses on either overlapped or discontinuous entities. In this paper, we propose a novel span-based model that can recognize both overlapped and discontinuous entities jointly. The model includes two major steps. First, entity fragments are recognized by traversing over all possible text spans, thus, overlapped entities can be recognized. Second, we perform relation classification to judge whether a given pair of entity fragments to be overlapping or succession. In this way, we can recognize not only discontinuous entities, and meanwhile doubly check the overlapped entities. As a whole, our model can be regarded as a relation extraction paradigm essentially. Experimental results on multiple benchmark datasets (i.e., CLEF, GENIA and ACE05) show that our model is highly competitive for overlapped and discontinuous NER.
BibTeX
@inproceedings{li-etal-2021-span,
title = "A Span-Based Model for Joint Overlapped and Discontinuous Named Entity Recognition",
author = "Li, Fei and
Lin, ZhiChao and
Zhang, Meishan and
Ji, Donghong",
editor = "Zong, Chengqing and
Xia, Fei and
Li, Wenjie and
Navigli, Roberto",
booktitle = "Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers)",
month = aug,
year = "2021",
address = "Online",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2021.acl-long.372/",
doi = "10.18653/v1/2021.acl-long.372",
pages = "4814--4828"
}