Text Classification Using Word-Based PPM Models
نویسنده
چکیده
Text classification is one of the most actual among the natural language processing problems. In this paper the application of word-based PPM (Prediction by Partial Matching) model for automatic content-based text classification is described. Our main idea is that words and especially word combinations are more relevant features for many text classification tasks. Key-words for a document in most cases are not just single words but combination of two or three words. The main result of the implemented experiments proved applicability of word-based PPM models for content-based text classification. Although in some cases the entropy difference which influenced the choice was rather small (several hundredths), most of the documents (up to 97%) were classified correctly.
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ورودعنوان ژورنال:
- The Computer Science Journal of Moldova
دوره 14 شماره
صفحات -
تاریخ انتشار 2006