Abstract
This paper focuses on the problem of choosing a representation of documents that can be suitable to induce more advanced semantic user profiles, in which concepts are used instead of keywords to represent user interests. We propose a method which integrates a word sense disambiguation algorithm based on the WordNet IS-A hierarchy, with two machine learning techniques to induce semantic user profiles, namely a relevance feedback method and a probabilistic one. The document representation proposed, that we called Bag-Of-Synsets improves the classic Bag-Of-Words approach, as shown by an extensive experimental session.
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Degemmis, M., Lops, P., Semeraro, G. (2006). Learning Semantic User Profiles from Text. In: Li, X., Zaïane, O.R., Li, Z. (eds) Advanced Data Mining and Applications. ADMA 2006. Lecture Notes in Computer Science(), vol 4093. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11811305_73
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DOI: https://doi.org/10.1007/11811305_73
Publisher Name: Springer, Berlin, Heidelberg
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