A boundary-aware neural model for nested named entity recognition

Changmeng ZHENG, Yi CAI, Jingyun XU, Ho-fung LEUNG, Guandong XU

Research output: Chapter in Book/Report/Conference proceedingChapters

99 Citations (Scopus)

Abstract

In natural language processing, it is common that many entities contain other entities inside them. Most existing works on named entity recognition (NER) only deal with flat entities but ignore nested ones. We propose a boundary-aware neural model for nested NER which leverages entity boundaries to predict entity categorical labels. Our model can locate entities precisely by detecting boundaries using sequence labeling models. Based on the detected boundaries, our model utilizes the boundary-relevant regions to predict entity categorical labels, which can decrease computation cost and relieve error propagation problem in layered sequence labeling model. We introduce multitask learning to capture the dependencies of entity boundaries and their categorical labels, which helps to improve the performance of identifying entities. We conduct our experiments on nested NER datasets and the experimental results demonstrate that our model outperforms other state-of-the-art methods. Copyright © 2019 Association for Computational Linguistics.

Original languageEnglish
Title of host publicationProceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)
Place of PublicationHong Kong, China
PublisherAssociation for Computational Linguistics (ACL)
Pages357-366
ISBN (Electronic)9781950737901
DOIs
Publication statusPublished - 2019

Citation

Zheng, C., Cai, Y., Xu, J., Leung, H.-F., & Xu, G. (2019). A boundary-aware neural model for nested named entity recognition. In Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP) (pp. 357-366). Association for Computational Linguistics (ACL). https://doi.org/10.18653/v1/D19-1034

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