Abstract-The explosion of textual documents on the Web (journals, social networks, blogs) generated a need to treat this mass of data to extract knowledge. We are interested in this paper in a particular treatment which is text categorization. We propose a conceptual representation of texts by using Wordnet for documents categorization. This representation is based on terms disambiguation by using Wordnet concepts. Disambiguated concepts are extracted from representative terms of a document, and three representations (terms, concepts, terms+ concepts) are applied with three training algorithms: SVM, Decision trees, KNN for the categorization. Experiments were applied on two corpora: 11 categories of reuters-21578 articles and 7 categories of 20 newsgroup discussion documents. The use of (terms+ concepts) gave better results for the three training algorithms and especially for the decision trees.
Ezreg et al. proposed a conceptual representation based on terms disambiguation for text classification using WordNet REF .
Conceptual Representation Using WordNet for Text Categorization
International Journal of Computer and Communication Engineering
Computer Science