Abstract
Distinguishing the confusable words is an essential task for Chinese teaching and publications proofreading. Existing studies have made progress in analyzing confusable words in the lexical semantic view. However, few effective automated methods are applied to solve this problem. This paper proposes a hybrid model to distinguish the confusable words in proofreading. Through ensemble learning, the model combines contextual and non-contextual features of the input sentence. Experimental results on two test sets demonstrate that the hybrid model achieves superior performance against other baseline methods including solely BERT.
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Our work is supported by Beijing Postdoctoral Research Foundation.
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Li, L., Song, P., Zhang, D., Zhao, D. (2022). A Hybrid Model for Chinese Confusable Words Distinguishing in Proofreading. In: Dong, M., Gu, Y., Hong, JF. (eds) Chinese Lexical Semantics. CLSW 2021. Lecture Notes in Computer Science(), vol 13249. Springer, Cham. https://doi.org/10.1007/978-3-031-06703-7_36
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DOI: https://doi.org/10.1007/978-3-031-06703-7_36
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