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Text Classification Using Word-Based PPM Models
Authors: Victoria Bobicev
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Abstract
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.V.Bobicev
Technical University of Moldova,
Phone: (373+2) 31-91-82
E-mail:
Technical University of Moldova,
Phone: (373+2) 31-91-82
E-mail:
Fulltext
– 0.16 Mb
Contents
- Local and Global Parsing with Functional X-bar Theory and SCD Linguistic Strategy (II.) Part II. Functional Generative Capacity, SCD Marker Classes, and Local / Global Segmentation / Parsing Algorithms
- Text Classification Using Word-Based PPM Models
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