Convolution kernels on constituent, dependency and sequential structures for relation extraction

Truc Vien T. Nguyen, Alessandro Moschitti, Giuseppe Riccardi

Research output: Contribution to conferencePaper

Abstract

This paper explores the use of innovative kernels based on syntactic and semantic structures for a target relation extraction task. Syntax is derived from constituent and dependency parse trees whereas semantics concerns to entity types and lexical sequences. We investigate the effectiveness of such representations in the automated relation extraction from texts. We process the above data by means of Support Vector Machines along with the syntactic tree, the partial tree and the word sequence kernels. Our study on the ACE 2004 corpus illustrates that the combination of the above kernels achieves high effectiveness and significantly improves the current state-of-the-art.

Original languageEnglish
Pages1378-1387
Number of pages10
Publication statusPublished - 1 Dec 2009
Event2009 Conference on Empirical Methods in Natural Language Processing, EMNLP 2009, Held in Conjunction with ACL-IJCNLP 2009 - Singapore, Singapore
Duration: 6 Aug 20097 Aug 2009

Other

Other2009 Conference on Empirical Methods in Natural Language Processing, EMNLP 2009, Held in Conjunction with ACL-IJCNLP 2009
CountrySingapore
CitySingapore
Period6/8/097/8/09

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ASJC Scopus subject areas

  • Computational Theory and Mathematics
  • Computer Science Applications
  • Information Systems

Cite this

Nguyen, T. V. T., Moschitti, A., & Riccardi, G. (2009). Convolution kernels on constituent, dependency and sequential structures for relation extraction. 1378-1387. Paper presented at 2009 Conference on Empirical Methods in Natural Language Processing, EMNLP 2009, Held in Conjunction with ACL-IJCNLP 2009, Singapore, Singapore.