Abstract
Aiming at more efficient search on the Internet, it seems adequate to deploy classification techniques using semantic resources restricting this search to the user’s domain of interest. In this work, we try to assess the impact of integrating semantic knowledge on text classification. This integration can be realized in different ways. The one we choose in this paper is the conceptualization. We examine the impact of the different conceptualization strategies on text classification using three traditional text classification methods: Rocchio, Support Vector Machines (SVMs) and Naïve Bayes (NB). We restrain our experimentation to the biomedical domain so conceptualization is applied on OHSUMED corpus, mapping terms in text to their corresponding concepts in UMLS Metathesaurus in order to take their meaning into consideration during text classification. Rocchio, SVMs, and NB are tested using different conceptualization strategies in order to evaluate their effect on classification. Preliminary results demonstrate promising improvements.
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Albitar, S., Fournier, S., Espinasse, B. (2012). The Impact of Conceptualization on Text Classification. In: Wang, X.S., Cruz, I., Delis, A., Huang, G. (eds) Web Information Systems Engineering - WISE 2012. WISE 2012. Lecture Notes in Computer Science, vol 7651. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-35063-4_24
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DOI: https://doi.org/10.1007/978-3-642-35063-4_24
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