融合TF-IDF和LDA的中文FastText短文本分类方法

doi:10.3969/j.issn.0255-8297.2019.03.008

Abstract

Abstract: FastText text classification model has the advantages of high speed and high efficiency, but its application in Chinese short text classification has the problem of low precision. To solve this problem, a Chinese FastText short text classification method integrating TF-IDF and LDA is proposed. In the input phase of FastText text classification model, the dictionaries generated after n-gram processing are filtered by TF-IDF, and corpus thematic analysis is conducted by LDA model, then the feature dictionary is supplemented according to the obtained results. Thus, the highly differentiated entries are biased in the process of computing the mean value of input word sequence vectors, making them more suitable for Chinese short text classification environment. The experimental results show that the proposed method has higher precision in Chinese short text classification.

Key words: Chinese short text classification, FastText, term frequency-inverse document frequency (TF-IDF), word vector, latent Dirichlet allocation (LDA)

CLC Number:

TP311

FENG Yong, QU Bohao, XU Hongyan, WANG Rongbing, ZHANG Yonggang. Chinese FastText Short Text Classification Method Integrating TF-IDF and LDA[J]. Journal of Applied Sciences, 2019, 37(3): 378-388.

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Chinese FastText Short Text Classification Method Integrating TF-IDF and LDA

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