文章基本信息

标题：Text Classification based on Discriminative-Semantic Features and Variance of Fuzzy Similarity
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作者：Pouyan Parsafard ; Hadi Veisi ; Niloofar Aflaki 等
期刊名称：International Journal of Intelligent Systems and Applications
印刷版ISSN：2074-904X
电子版ISSN：2074-9058
出版年度：2022
卷号：14
期号：2
DOI：10.5815/ijisa.2022.02.03
语种：English
出版社：MECS Publisher
摘要：Due to the rapid growth of the Internet, large amounts of unlabelled textual data are producing daily. Clearly, finding the subject of a text document is a primary source of information in the text processing applications. In this paper, a text classification method is presented and evaluated for Persian and English. The proposed technique utilizes variance of fuzzy similarity besides discriminative and semantic feature selection methods. Discriminative features are those that distinguish categories with higher power and the concept of semantic feature takes into the calculations the similarity between features and documents by using only available documents. In the proposed method, incorporating fuzzy weighting as a measure of similarity is presented. The fuzzy weights are derived from the concept of fuzzy similarity which is defined as the variance of membership values of a document to all categories in the way that with some membership value at the same time, the sum of these membership values should be equal to 1. The proposed document classification method is evaluated on three datasets (one Persian and two English datasets) and two classification methods, support vector machine (SVM) and artificial neural network (ANN), are used. Comparing the results with other text classification methods, demonstrate the consistent superiority of the proposed technique in all cases. The weighted average F-measure of our method are %82 and %97.8 in the classification of Persian and English documents, respectively.
关键词：Persian topic identification;Discriminative features;Semantic similarities;Fuzzy similarities;Natural language processing