Abstract
In this paper, we present the effect of the semantic indexing using WordNet senses on the Information Retrieval (IR) and Text Categorization (TC) tasks. The documents have been sense-tagged using a Word Sense Disambiguation (WSD) system based on Specialized Hidden Markov Models (SHMMs). The preliminary results showed that a small improvement of the performance was obtained only in the TC task.
This work was supported by the Spanish Research Projects CICYT TIC2000-0664-C02 and TIC2003-07158-C04-03. We are grateful to E. Ferretti for sense-tagging the data.
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Rosso, P., Molina, A., Pla, F., Jiménez, D., Vidal, V. (2004). Information Retrieval and Text Categorization with Semantic Indexing. In: Gelbukh, A. (eds) Computational Linguistics and Intelligent Text Processing. CICLing 2004. Lecture Notes in Computer Science, vol 2945. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-24630-5_73
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DOI: https://doi.org/10.1007/978-3-540-24630-5_73
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