Combined syntactic and semantic kernels for text classification

Stephan Bloehdorn, Alessandro Moschitti

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

23 Citations (Scopus)

Abstract

The exploitation of syntactic structures and semantic background knowledge has always been an appealing subject in the context of text retrieval and information management. The usefulness of this kind of information has been shown most prominently in highly specialized tasks, such as classification in Question Answering (QA) scenarios. So far, however, additional syntactic or semantic information has been used only individually. In this paper, we propose a principled approach for jointly exploiting both types of information. We propose a new type of kernel, the Semantic Syntactic Tree Kernel (SSTK), which incorporates linguistic structures, e.g. syntactic dependencies, and semantic background knowledge, e.g. term similarity based on WordNet, to automatically learn question categories in QA. We show the power of this approach in a series of experiments with a well known Question Classification dataset.

Original languageEnglish
Title of host publicationAdvances in Information Retrieval - 29th European Conference on IR Research, ECIR 2007, Proceedings
Pages307-318
Number of pages12
Publication statusPublished - 20 Dec 2007
Event29th European Conference on IR Research, ECIR 2007 - Rome, Italy
Duration: 2 Apr 20075 Apr 2007

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume4425 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Other

Other29th European Conference on IR Research, ECIR 2007
CountryItaly
CityRome
Period2/4/075/4/07

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ASJC Scopus subject areas

  • Theoretical Computer Science
  • Computer Science(all)

Cite this

Bloehdorn, S., & Moschitti, A. (2007). Combined syntactic and semantic kernels for text classification. In Advances in Information Retrieval - 29th European Conference on IR Research, ECIR 2007, Proceedings (pp. 307-318). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 4425 LNCS).