Abstract
Context-based Web search has become an important research area and many strategies have been proposed to reflect contextual information in search queries. Despite the success of some of these proposals they still have serious limitations due to their inability to bridge the terminology gap existing between the user context description and the relevant documents’ vocabulary. This paper presents a quantitative technique to learn vocabularies useful for describing the theme of a context under analysis. The enriched vocabulary allows the formulation of search queries to identify resources with higher precision than those identified using the initial vocabulary. Rigorous experimentation leads us to conclude that the proposed technique is superior to a baseline and other well-known query reformulation techniques.
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These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.
This research work is supported by Agencia Nacional de Promoción Científica y Tecnológica (PICT 2005 Nro. 32373) and Universidad Nacional del Sur (PGI 24/ZN13).
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Lorenzetti, C.M., Maguitman, A.G. (2008). Tuning Topical Queries through Context Vocabulary Enrichment: A Corpus-Based Approach. In: Meersman, R., Tari, Z., Herrero, P. (eds) On the Move to Meaningful Internet Systems: OTM 2008 Workshops. OTM 2008. Lecture Notes in Computer Science, vol 5333. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-88875-8_88
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DOI: https://doi.org/10.1007/978-3-540-88875-8_88
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