Abstract
Natural language processing (NLP) is a problem which attracts lots of attention from researchers. This study analyzes and compares a different method to classify text sentences or paragraphs in Vietnamese into different categories. The work utilizes a sequence of techniques for data-preprocessing, customize learning model and methods before using Term Frequency-Inverse Document Frequency (TF-IDF) for model training. This classification model could contribute positively to many Vietnamese text-analyzing based businesses, such as social network, e-commerce, or data mining in general. This problem’s challenge relies on two main aspects: the Vietnamese language itself and current NLP researches for the Vietnamese language. The paper utilizes the pros of many different classification methods to provide better accuracy in text classification.
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Quan, D.V., Hung, P.D. (2021). Application of Customized Term Frequency-Inverse Document Frequency for Vietnamese Document Classification in Place of Lemmatization. In: Vasant, P., Zelinka, I., Weber, GW. (eds) Intelligent Computing and Optimization. ICO 2020. Advances in Intelligent Systems and Computing, vol 1324. Springer, Cham. https://doi.org/10.1007/978-3-030-68154-8_37
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DOI: https://doi.org/10.1007/978-3-030-68154-8_37
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