Automatic semantic relation extraction with multiple boundary generation

Brandon Beamer, Alia Rozovskaya, Roxana Girju

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

Abstract

This paper addresses the task of automatic classification of semantic relations between nouns. We present an improved WordNet-based learning model which relies on the semantic information of the constituent nouns. The representation of each noun's meaning captures conceptual features which play a key role in the identification of the semantic relation. We report substantial improvements over previous WordNet-based methods on the 2007 SemEval data. Moreover, our experiments show that WordNet's IS-A hierarchy is better suited for some semantic relations compared with others. We also compute various learning curves and show that our model does not need a large number of training examples.

Original languageEnglish (US)
Title of host publicationNational Conference on Artificial Intelligence
Pages824-829
Number of pages6
StatePublished - Dec 24 2008
Event23rd AAAI Conference on Artificial Intelligence and the 20th Innovative Applications of Artificial Intelligence Conference, AAAI-08/IAAI-08 - Chicago, IL, United States
Duration: Jul 13 2008Jul 17 2008

Publication series

NameProceedings of the National Conference on Artificial Intelligence
Volume2

Other

Other23rd AAAI Conference on Artificial Intelligence and the 20th Innovative Applications of Artificial Intelligence Conference, AAAI-08/IAAI-08
CountryUnited States
CityChicago, IL
Period7/13/087/17/08

ASJC Scopus subject areas

  • Software
  • Artificial Intelligence

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