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With the accumulation of various kinds of text data, it is no longer possible to generalize or classify them by manual reading, so how to use statistical models to mine text data reasonably and effectively has become an important issue in academic research and practical work. This paper discusses three problems of Chinese text mining: word separation, keyword extraction and text classification. For the word separation problem, the Cascaded Hidden Markov Model and the WDM that treats the segmentation between words as missing data and solves it with the EM algorithm are introduced. For the keyword extraction problem, this paper proposes a Bayes factor and introduces CCS using sparse regression. For the text classification problem, the method of building a classifier based on the frequency of keywords and the method of building a classifier based on the probability of the topic first are introduced. We give the respective advantages of each method by comparing the above methods with two datasets using SVM and Random forest, and make suggestions of their use. © 2023 SPIE.
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ISSN: 0277-786X
年份: 2023
卷: 12597
语种: 英文
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