Kernel methods, syntax and semantics for relational text categorization

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

67 Citations (Scopus)

Abstract

Previous work on Natural Language Processing for Information Retrieval has shown the inadequateness of semantic and syntactic structures for both document retrieval and categorization. The main reason is the high reliability and effectiveness of language models, which are sufficient to accurately solve such retrieval tasks. However, when the latter involve the computation of relational semantics between text fragments simple statistical models may result ineffective. In this paper, we show that syntactic and semantic structures can be used to greatly improve complex categorization tasks such as determining if an answer correctly responds to a question. Given the high complexity of representing semantic/syntactic structures in learning algorithms, we applied kernel methods along with Support Vector Machines to better exploit the needed relational information. Our experiments on answer classification on Web and TREC data show that our models greatly improve on bagof-words.

Original languageEnglish
Title of host publicationInternational Conference on Information and Knowledge Management, Proceedings
Pages253-262
Number of pages10
DOIs
Publication statusPublished - 1 Dec 2008
Externally publishedYes
Event17th ACM Conference on Information and Knowledge Management, CIKM'08 - Napa Valley, CA, United States
Duration: 26 Oct 200830 Oct 2008

Other

Other17th ACM Conference on Information and Knowledge Management, CIKM'08
CountryUnited States
CityNapa Valley, CA
Period26/10/0830/10/08

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Keywords

  • Kernel methods
  • Natural language processing
  • Question answering
  • Support vector machines
  • Text categorization

ASJC Scopus subject areas

  • Business, Management and Accounting(all)
  • Decision Sciences(all)

Cite this

Moschitti, A. (2008). Kernel methods, syntax and semantics for relational text categorization. In International Conference on Information and Knowledge Management, Proceedings (pp. 253-262) https://doi.org/10.1145/1458082.1458118