Huang, Z., Rong, W., Zhang, X. et al. (3 more authors) (2022) Token relation aware Chinese named entity recognition. ACM Transactions on Asian and Low-Resource Language Information Processing. ISSN 2375-4699
Abstract
Due to the lack of natural delimiters, most Chinese Named Entity Recognition (NER) approaches are character-based and utilize an external lexicon to leverage the word-level information. Although they have achieved promising results, the latent words they introduced are still non-contextualized. In this paper, we investigate three relations, i.e, adjacent relation between characters, character co-occurrence relation between latent words, and dependency relation among tokens, to address this issue. Specifically, we first establish the local context for latent words and then propose a masked self-attention mechanism to incorporate such local contextual information. Besides, since introducing external knowledge such as lexicon and dependency relation inevitably brings in some noises, we propose a gated information controller to handle this problem. Extensive experimental results show that the proposed approach surpasses most similar methods on public datasets and demonstrates its promising potential.
Metadata
Authors/Creators: |
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Copyright, Publisher and Additional Information: | © 2022 Association for Computing Machinery. |
Dates: |
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Institution: | The University of Sheffield |
Academic Units: | The University of Sheffield > Faculty of Engineering (Sheffield) > Department of Computer Science (Sheffield) |
Depositing User: | Symplectic Sheffield |
Date Deposited: | 23 Nov 2022 16:40 |
Last Modified: | 23 Nov 2022 16:40 |
Status: | Published online |
Publisher: | Association for Computing Machinery (ACM) |
Refereed: | Yes |
Identification Number: | https://doi.org/10.1145/3531534 |