A Boundary-aware Neural Model for Nested Named Entity Recognition
Refereed conference paper presented and published in conference proceedings


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AbstractIn 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.
All Author(s) ListChangmeng Zheng, Yi Cai, Jingyun Xu, Ho-fung Leung, Guandong Xu
Name of ConferenceThe 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)
Start Date of Conference03/11/2019
End Date of Conference07/11/2019
Place of ConferenceHong Kong
Country/Region of ConferenceHong Kong
Proceedings TitleEMNLP-IJCNLP 2019 - 2019 Conference on Empirical Methods in Natural Language Processing and 9th International Joint Conference on Natural Language Processing, Proceedings of the Conference
Year2019
Pages357 - 366
ISBN978-195073790-1
LanguagesEnglish-United States

Last updated on 2020-01-08 at 02:03