Abstract
Word sense disambiguation (WSD) is a well-known task in the field of natural language processing. It attempts to determine a meaning of a word that has a couple of senses. This paper studies the Chinese word sense disambiguation by employing supervised classification method. Initially, feature selection is performed based on feature windows. Three types of features are extracted in this research: part-of-speech (POS), words, and 2-gram collocation. Further, we make a comparison of different classification algorithms, including sequential minimal optimization (SMO), naïve bayes, and multilayer perceptron (MLP). Different parameters are tested in order to obtain best precision of dataset. Additionally, a punctuation optimization approach is proposed to refine the final classification precision. Experimental results show that our method can achieve a good effect. The proposed approach contributes a lot to the WSD task by exploring feature selection as well as punctuation optimization.
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Acknowledgement
This work was supported by the High-level Innovation and Entrepreneurship Talents Introduction Program of Jiangsu Province of China, 2019.
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Fan, C., Li, Y. (2021). Chinese Word Sense Disambiguation Based on Classification. In: Huang, DS., Jo, KH., Li, J., Gribova, V., Hussain, A. (eds) Intelligent Computing Theories and Application. ICIC 2021. Lecture Notes in Computer Science(), vol 12837. Springer, Cham. https://doi.org/10.1007/978-3-030-84529-2_37
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DOI: https://doi.org/10.1007/978-3-030-84529-2_37
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