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Improve Chinese Spelling Check by Reevaluation

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Advances in Knowledge Discovery and Data Mining (PAKDD 2022)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 13282))

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Abstract

Chinese Spelling Check (CSC) aims to detect and correct the spelling errors in Chinese. Most Chinese spelling errors are misused semantically, phonetically or graphically similar characters. Previous state-of-the-art works on the CSC task pursue transitions from misspelled sentences to correct sentences directly. However, the spelling errors, especially the continuous incorrect characters, usually confuse the meaning of the semantic context. It is difficult to make correct modifications for CSC models based on the error contextual information. To address this issue, we propose a simple but effective pipeline for CSC by searching the most appropriate candidate sentences as the original correct sentence. Specifically, candidate sentences are generated based on possible error characters with the confusion set. Then we reevaluate the candidate sentences to find the best in terms of character probabilities and similarity compared to the original error characters. Besides, we extend the widely used confusion set (The code and data are available at https://github.com/zuoyecihua/CSC.). Simply applying the confusion set as a filter will bring large performance improvement. The experimental results show that our approach outperforms previous methods and performs well on bi-gram errors.

This work is supported by the National Natural Science Foundation of China (No. 61672276, No. 51975294).

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Notes

  1. 1.

    https://github.com/BYVoid/OpenCC.

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Authors and Affiliations

  1. State Key Laboratory for Novel Software Technology, Nanjing University, Nanjing, 210023, China

    Shuai Wang & Lin Shang

  2. Department of Computer Science and Technology, Nanjing University, Nanjing, 210023, China

    Shuai Wang & Lin Shang

Authors
  1. Shuai Wang
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  2. Lin Shang
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Corresponding author

Correspondence to Lin Shang .

Editor information

Editors and Affiliations

  1. Laboratory of Artificial Intelligence and Decision Support, University of Porto, Porto, Portugal

    João Gama

  2. School of Computing and Artificial Intelligence, Southwest Jiaotong University, Chengdu, China

    Tianrui Li

  3. National Key Laboratory for Novel Software Technology, Nanjing University, Nanjing, China

    Yang Yu

  4. School of Computer Science and Technology, University of Science and Technology of China, Hefei, China

    Enhong Chen

  5. JD iCity, JD Technology & JD Intelligent Cities Research, Beijing, China

    Yu Zheng

  6. School of Computing and Artificial Intelligence, Southwest Jiaotong University, Chengdu, China

    Fei Teng

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Wang, S., Shang, L. (2022). Improve Chinese Spelling Check by Reevaluation. In: Gama, J., Li, T., Yu, Y., Chen, E., Zheng, Y., Teng, F. (eds) Advances in Knowledge Discovery and Data Mining. PAKDD 2022. Lecture Notes in Computer Science(), vol 13282. Springer, Cham. https://doi.org/10.1007/978-3-031-05981-0_19

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  • DOI: https://doi.org/10.1007/978-3-031-05981-0_19

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  • Online ISBN: 978-3-031-05981-0

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