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A Radical-Based Method for Chinese Named Entity Recognition

Published: 28 August 2019 Publication History

Abstract

Chinese characters are composed of radicals, and their radicals have the distinction between "shaped parts" (representing semantics) and "sound parts" (representing speech). As a hieroglyph, many radicals of Chinese characters have certain semantic information, which can effectively improve the performance of Chinese named entity recognition. In the Chinese named entity recognition, many related studies use Bi-LSTM to extract the semantic features from radicals. However, the LSTM-based model cannot effectively extract the semantic information of radicals due to ambiguity in partitioning the granularity of radicals and weak dependency between Chinese radicals. Therefore, this paper presents a radical neural network method RCBC (Radical CNN-BiLSTM-CRF). The experimental results on SIGHAN 2006 Bakeoff MSRA dataset and Peking University's People's Daily dataset in 1998 indicate that this model can effectively extract the semantic information of Chinese radicals and improve the performanceof Chinese named entity recognition compared with the traditional model.

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  • (2022)An Entity Relation Extraction Method for Few-Shot Learning on the Food Health and Safety DomainComputational Intelligence and Neuroscience10.1155/2022/18794832022Online publication date: 1-Jan-2022
  • (2022)Radial Basis Function Attention for Named Entity RecognitionACM Transactions on Asian and Low-Resource Language Information Processing10.1145/353901422:1(1-18)Online publication date: 30-Nov-2022
  • (2022)AIP: A Named Entity Recognition Method Combining Glyphs and SoundsACM Transactions on Asian and Low-Resource Language Information Processing10.1145/352273621:6(1-14)Online publication date: 12-Nov-2022
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    cover image ACM Other conferences
    ICBDT '19: Proceedings of the 2nd International Conference on Big Data Technologies
    August 2019
    382 pages
    ISBN:9781450371926
    DOI:10.1145/3358528
    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 ACM 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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    • Shandong Univ.: Shandong University

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    Published: 28 August 2019

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    Author Tags

    1. Bi-LSTM
    2. Chinese nmed etity rcognition
    3. RCBC
    4. Radicals of characters

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    Cited By

    View all
    • (2022)An Entity Relation Extraction Method for Few-Shot Learning on the Food Health and Safety DomainComputational Intelligence and Neuroscience10.1155/2022/18794832022Online publication date: 1-Jan-2022
    • (2022)Radial Basis Function Attention for Named Entity RecognitionACM Transactions on Asian and Low-Resource Language Information Processing10.1145/353901422:1(1-18)Online publication date: 30-Nov-2022
    • (2022)AIP: A Named Entity Recognition Method Combining Glyphs and SoundsACM Transactions on Asian and Low-Resource Language Information Processing10.1145/352273621:6(1-14)Online publication date: 12-Nov-2022
    • (2021)MAF-CNER Complexity10.1155/2021/66960642021Online publication date: 1-Jan-2021
    • (2020)MoGCN: Mixture of Gated Convolutional Neural Network for Named Entity Recognition of Chinese Historical TextsIEEE Access10.1109/ACCESS.2020.30265358(181629-181639)Online publication date: 2020

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