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Think More Ambiguity Less: A Novel Dual Interactive Model with Local and Global Semantics for Chinese Named Entity Recognition

Published:17 June 2023Publication History
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Abstract

Chinese is a representative East Asian language. Chinese Named Entity Recognition (CNER) aims to recognize various entities. It is significant for other NLP tasks to utilize CNER. Recent research to develop CNER systems has been dedicated to either considering word enhancement or capturing global information to strengthen local composition and alleviate word ambiguity in the meanings of words. However, information on words acquired from external lexicons is often confused, and this has led to incorrect judgments regarding the boundaries of words. Moreover, relevant studies typically use excessively complex models to capture the global semantics of sentences. To solve these two problems, we incorporate a global representation into the procedure of local word enhancement. We propose an intuitive and effective dual-module interactive network that can enhance the boundaries of words and extract the global semantics by using a rethinking mechanism to refine the importance of local composition and global information. The results of experiments on four CNER datasets showed that the proposed model can outperform other baselines in terms of the F1 score.

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  1. Think More Ambiguity Less: A Novel Dual Interactive Model with Local and Global Semantics for Chinese Named Entity Recognition

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    • Published in

      cover image ACM Transactions on Asian and Low-Resource Language Information Processing
      ACM Transactions on Asian and Low-Resource Language Information Processing  Volume 22, Issue 6
      June 2023
      635 pages
      ISSN:2375-4699
      EISSN:2375-4702
      DOI:10.1145/3604597
      Issue’s Table of Contents

      Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than the author(s) must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected].

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      Association for Computing Machinery

      New York, NY, United States

      Publication History

      • Published: 17 June 2023
      • Online AM: 10 February 2023
      • Revised: 5 February 2023
      • Accepted: 5 February 2023
      • Received: 21 April 2022
      Published in tallip Volume 22, Issue 6

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