User Modelling for News Web Sites with Word Sense Based Techniques

SiteIF is a personal agent for a bilingual news web site that learns user’s interests from the requested pages. In this paper we propose to use a word sense based document representation as a starting point to build a model of the user’s interests. Documents passed over are processed and relevant senses (disambiguated over WordNet) are extracted and then combined to form a semantic network. A filtering procedure dynamically predicts new documents on the basis of the semantic network.There are two main advantages of a sense-based approach: first, the model predictions, being based on senses rather than words, are more accurate; second, the model is language independent, allowing navigation in multilingual sites. We report the results of a comparative experiment that has been carried out to give a quantitative estimation of these improvements.

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