A comprehensive analysis of using semantic information in text categorization
2013 IEEE International Symposium on Innovations in Intelligent Systems and Applications, IEEE INISTA 2013, Albena, Bulgaristan, 19 - 21 Haziran 2013, (Tam Metin Bildiri)
- Yayın Türü: Bildiri / Tam Metin Bildiri
- Doi Numarası: 10.1109/inista.2013.6577651
- Basıldığı Şehir: Albena
- Basıldığı Ülke: Bulgaristan
- Anahtar Kelimeler: pos tagging, semantic, text categorization, word sense disambiguation, wordnet
- Boğaziçi Üniversitesi Adresli: Evet
Özet
Traditional text categorization methods only deal with the content of the documents and use some statistic based metrics to represent the documents. The representation is then used by a machine learning approach to determine the document class. In this picture, the meaning of the document is missing. In order to add meaning into the text categorization process, we start with using part-of-speech tagging (POS). As expected, in a document each part-of-speech tag does not contribute the same amount of information to the document meaning. In addition to the POS information, we make use of WordNet to add semantic features such as synonyms, hypernyms, hyponyms, meronyms and topics into classification process. Using WordNet's semantic features introduces ambiguity and not all semantic features are really related to the document content. To overcome this problem, we introduce a new method to eliminate the ambiguity. Various combinations of POS, WordNet and word sense disambiguation are applied and the results show that using semantic features perform better than the traditional, context based methods. © 2013 IEEE.