ECNU: Effective semantic relations classification without complicated features or multiple external corpora

  • Yuan Chent
  • , Man Lan
  • , Jian Su
  • , Zhi Min Zhou
  • , Yu Xu

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

7 Scopus citations

Abstract

This paper describes our approach to the automatic identification of semantic relations between nominals in English sentences. The basic idea of our strategy is to develop machine-learning classifiers which: (1) make use of class-independent features and classifier; (2) make use of a simple and effective feature set without high computational cost; (3) make no use of external annotated or unannotated corpus at all. At SemEval 2010 Task 8 our system achieved an F-measure of 75.43% and a accuracy of 70.22%.

Original languageEnglish
Title of host publicationACL 2010 - SemEval 2010 - 5th International Workshop on Semantic Evaluation, Proceedings
PublisherAssociation for Computational Linguistics (ACL)
Pages226-229
Number of pages4
ISBN (Electronic)1932432701, 9781932432701
StatePublished - 2010
Event5th International Workshop on Semantic Evaluation, SemEval 2010 - Uppsala, Sweden
Duration: 15 Jul 201016 Jul 2010

Publication series

NameACL 2010 - SemEval 2010 - 5th International Workshop on Semantic Evaluation, Proceedings

Conference

Conference5th International Workshop on Semantic Evaluation, SemEval 2010
Country/TerritorySweden
CityUppsala
Period15/07/1016/07/10

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