Employing Latent Dirichlet Allocation Model for Topic Extraction of Chinese Text
Qihua Liu · International Journal of Database Theory and Application · 2016
The hidden topic model of Chinese text, which possesses complicated semantics, is urgently needed, since China has occupied an increasingly significant role during the booming development of globalization over recent years. This paper details and elaborates the basic process of extracting latent Chinese topics by demonstrating a Chinese topic extraction schema based on Latent Dirichlet Allocation (LDA) model. Furthermore, the application was practiced in CCL, an authoritative Chinese corpus, to extract topics for its nine categories. With rigorous empirical analysis, extracting the LDA results has a considerably higher average precision rate as opposed to other three comparable Chinese topic extraction techniques; however the average recall rate is worse than KNN and almost the same with the PLSI model. Moreover, the recall rate and precision rate of LDA-CH is worse than LDA-EH. Therefore, the LDA model should be improved to adapt to the distinctive feature of Chinese words with the purpose of making it better for Chinese topic extraction.