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A Hybrid Model for Chinese Spelling Check

Authors:

Zhongye JiaAuthors Info & Claims

ACM Transactions on Asian and Low-Resource Language Information Processing (TALLIP), Volume 16, Issue 3

Article No.: 21, Pages 1 - 22

https://doi.org/10.1145/3047405

Published: 30 March 2017 Publication History

Abstract

Spelling check for Chinese has more challenging difficulties than that for other languages. A hybrid model for Chinese spelling check is presented in this article. The hybrid model consists of three components: one graph-based model for generic errors and two independently trained models for specific errors. In the graph model, a directed acyclic graph is generated for each sentence, and the single-source shortest-path algorithm is performed on the graph to detect and correct general spelling errors at the same time. Prior to that, two types of errors over functional words (characters) are first solved by conditional random fields: the confusion of “在” (at) (pinyin is zai in Chinese), “再” (again, more, then) (pinyin: zai) and “的” (of) (pinyin: de), “地” (-ly, adverb-forming particle) (pinyin: de), and “得” (so that, have to) (pinyin: de). Finally, a rule-based model is exploited to distinguish pronoun usage confusion: “她” (she) (pinyin: ta), “他” (he) (pinyin: ta), and some other common collocation errors. The proposed model is evaluated on the standard datasets released by the SIGHAN Bake-off shared tasks, giving state-of-the-art results.

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Cited By

B AOluwaseyi EB AM OAbiodun OO BBadeji-Ajisafe BA AA S(2024)Automatic Spelling Corrector for Yorùbá Language Using Edit Distance and N-Gram Language Models2024 International Conference on Science, Engineering and Business for Driving Sustainable Development Goals (SEB4SDG)10.1109/SEB4SDG60871.2024.10630121(1-6)Online publication date: 2-Apr-2024
https://doi.org/10.1109/SEB4SDG60871.2024.10630121
Li RXiang XHu BDeng XZhao J(2024)DBCERT: Reconstruct the BERT Model for Chinese Spelling Correction2024 IEEE International Symposium on Product Compliance Engineering - Asia (ISPCE-ASIA)10.1109/ISPCE-ASIA64773.2024.10756270(1-6)Online publication date: 25-Oct-2024
https://doi.org/10.1109/ISPCE-ASIA64773.2024.10756270
杜晓(2023)Research on Chinese Proofreading Technology with Feed-Back MechanismComputer Science and Application10.12677/CSA.2023.13303713:03(390-398)Online publication date: 2023
https://doi.org/10.12677/CSA.2023.133037
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Index Terms

A Hybrid Model for Chinese Spelling Check
1. Computing methodologies
  1. Artificial intelligence
    1. Natural language processing

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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 16, Issue 3

September 2017

167 pages

ISSN:2375-4699

EISSN:2375-4702

DOI:10.1145/3041821

Editor:
Nianwen Xue
Brandeis University, Waltham, USA

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Copyright © 2017 ACM.

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 30 March 2017

Accepted: 01 January 2017

Revised: 01 November 2016

Received: 01 July 2016

Published in TALLIP Volume 16, Issue 3

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Funding Sources

Major Basic Research Program of Shanghai Science and Technology Committee
National Basic Research Program of China
National Natural Science Foundation of China
Cai Yuanpei Program
Art and Science Interdisciplinary Funds of Shanghai Jiao Tong University
Key Project of the National Society Science Foundation of China

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Cited By

B AOluwaseyi EB AM OAbiodun OO BBadeji-Ajisafe BA AA S(2024)Automatic Spelling Corrector for Yorùbá Language Using Edit Distance and N-Gram Language Models2024 International Conference on Science, Engineering and Business for Driving Sustainable Development Goals (SEB4SDG)10.1109/SEB4SDG60871.2024.10630121(1-6)Online publication date: 2-Apr-2024
https://doi.org/10.1109/SEB4SDG60871.2024.10630121
Li RXiang XHu BDeng XZhao J(2024)DBCERT: Reconstruct the BERT Model for Chinese Spelling Correction2024 IEEE International Symposium on Product Compliance Engineering - Asia (ISPCE-ASIA)10.1109/ISPCE-ASIA64773.2024.10756270(1-6)Online publication date: 25-Oct-2024
https://doi.org/10.1109/ISPCE-ASIA64773.2024.10756270
杜晓(2023)Research on Chinese Proofreading Technology with Feed-Back MechanismComputer Science and Application10.12677/CSA.2023.13303713:03(390-398)Online publication date: 2023
https://doi.org/10.12677/CSA.2023.133037
Lin WHan MJin T(2023)Multi-stage Legal Instrument Grammatical Error Correction via Seq2Edit and Data AugmentationProceedings of the 2023 6th International Conference on Machine Learning and Natural Language Processing10.1145/3639479.3639524(215-220)Online publication date: 27-Dec-2023
https://dl.acm.org/doi/10.1145/3639479.3639524
Lertpiya AChalothorn TBuabthong P(2023)How to Progressively Build Thai Spelling Correction Systems?IEEE Access10.1109/ACCESS.2023.329500411(72704-72716)Online publication date: 2023
https://doi.org/10.1109/ACCESS.2023.3295004
Jiang WYe ZOu ZZhao RZheng JLiu YLiu BLi SYang YZheng YAl Hasan MXiong L(2022)MCSCSet: A Specialist-annotated Dataset for Medical-domain Chinese Spelling CorrectionProceedings of the 31st ACM International Conference on Information & Knowledge Management10.1145/3511808.3557636(4084-4088)Online publication date: 17-Oct-2022
https://dl.acm.org/doi/10.1145/3511808.3557636
Zhao GGuo YXia FMa C(2022)A Multimodal Method for Chinese Spelling Correction2022 International Joint Conference on Neural Networks (IJCNN)10.1109/IJCNN55064.2022.9892297(01-07)Online publication date: 18-Jul-2022
https://doi.org/10.1109/IJCNN55064.2022.9892297
Sun XZhou JWang SLi HJia JZhu J(2022)Chinese Spelling Error Detection and Correction Based on Knowledge GraphDatabase Systems for Advanced Applications. DASFAA 2022 International Workshops10.1007/978-3-031-11217-1_11(149-159)Online publication date: 16-Jul-2022
https://doi.org/10.1007/978-3-031-11217-1_11
Gou WChen Z(2021)Think Twice: A Post-Processing Approach for the Chinese Spelling Error CorrectionApplied Sciences10.3390/app1113583211:13(5832)Online publication date: 23-Jun-2021
https://doi.org/10.3390/app11135832
Nguyen MNgo GChen N(2021)Domain-Shift Conditioning Using Adaptable Filtering Via Hierarchical Embeddings for Robust Chinese Spell CheckIEEE/ACM Transactions on Audio, Speech, and Language Processing10.1109/TASLP.2021.308310829(2027-2036)Online publication date: 2021
https://doi.org/10.1109/TASLP.2021.3083108
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