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Open Access

Leveraging Integrated Learning for Open-Domain Chinese Named Entity Recognition

Jin Diao1, Zhangbing Zhou1,2( ), Guangli Shi1
School of Information Engineering, China University of Geosciences, Beijing 100083, China
TELECOM SudParis, Evry 91011, France
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Abstract

Named entity recognition (NER) is a fundamental technique in natural language processing that provides preconditions for tasks, such as natural language question reasoning, text matching, and semantic text similarity. Compared to English, the challenge of Chinese NER lies in the noise impact caused by the complex meanings, diverse structures, and ambiguous semantic boundaries of the Chinese language itself. At the same time, compared with specific domains, open-domain entity types are more complex and changeable, and the number of entities is considerably larger. Thus, the task of Chinese NER is more difficult. However, existing open-domain NER methods have low recognition rates. Therefore, this paper proposes a method based on the bidirectional long short-term memory conditional random field (BiLSTM-CRF) model, which leverages integrated learning to improve the efficiency of Chinese NER. Compared with single models, including CRF, BiLSTM-CRF, and gated recurrent unit-CRF, the proposed method can significantly improve the accuracy of open-domain Chinese NER.

Keywords

Chinese named entity recognitionintegrated learningopen-domain

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International Journal of Crowd Science
Volume 6 Issue 2,
June 2022
Pages 74-79
DOI: 10.26599/IJCS.2022.9100015
Cite this article:
Diao J, Zhou Z, Shi G. Leveraging Integrated Learning for Open-Domain Chinese Named Entity Recognition. International Journal of Crowd Science, 2022, 6(2): 74-79. https://doi.org/10.26599/IJCS.2022.9100015

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Received: 04 January 2022
Revised: 25 April 2022
Accepted: 26 April 2022
Published: 30 June 2022
© The author(s) 2022

The articles published in this open access journal are distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/).
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