A semantic kernel to exploit linguistic knowledge

Roberto Basili, Marco Cammisa, Alessandro Moschitti

Research output: Chapter in Book/Report/Conference proceedingConference contribution

5 Citations (Scopus)

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.

Original languageEnglish
Title of host publicationLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Pages290-302
Number of pages13
DOIs
Publication statusPublished - 1 Dec 2005
Event9th Congress of the Italian Association for Artificial Intelligence - AI/IA 2005: Advances in Artificial Intelligence - Milan, Italy
Duration: 21 Sep 200523 Sep 2005

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume3673 LNAI
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Other

Other9th Congress of the Italian Association for Artificial Intelligence - AI/IA 2005: Advances in Artificial Intelligence
CountryItaly
CityMilan
Period21/9/0523/9/05

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ASJC Scopus subject areas

  • Theoretical Computer Science
  • Computer Science(all)

Cite this

Basili, R., Cammisa, M., & Moschitti, A. (2005). A semantic kernel to exploit linguistic knowledge. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (pp. 290-302). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 3673 LNAI). https://doi.org/10.1007/11558590_30