Abstract
Improving accuracy in Information Retrieval tasks via semantic information is a complex problem characterized by three main aspects: the document representation model, the similarity estimation metric and the inductive algorithm. In this paper an original kernel function sensitive to external semantic knowledge is defined as a document similarity model. This semantic kernel was tested over a text categorization task, under critical learning conditions (i.e. poor training data). The results of cross-validation experiments suggest that the proposed kernel function can be used as a general model of document similarity for IR tasks.
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Basili, R., Cammisa, M., Moschitti, A. (2005). A Semantic Kernel to Exploit Linguistic Knowledge. In: Bandini, S., Manzoni, S. (eds) AI*IA 2005: Advances in Artificial Intelligence. AI*IA 2005. Lecture Notes in Computer Science(), vol 3673. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11558590_30
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DOI: https://doi.org/10.1007/11558590_30
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-29041-4
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