Classification of semantic relationships between nominals using pattern clusters

Dmitry Davidov*, Ari Rappoport

*Corresponding author for this work

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

43 Scopus citations

Abstract

There are many possible different semantic relationships between nominals. Classification of such relationships is an important and difficult task (for example, the well known noun compound classification task is a special case of this problem). We propose a novel pattern clusters method for nominal relationship (NR) classification. Pattern clusters are discovered in a large corpus independently of any particular training set, in an unsupervised manner. Each of the extracted clusters corresponds to some unspecified semantic relationship. The pattern clusters are then used to construct features for training and classification of specific inter-nominal relationships. Our NR classification evaluation strictly follows the ACL SemEval-07 Task 4 datasets and protocol, obtaining an f-score of 70.6, as opposed to 64.8 of the best previous work that did not use the manually provided WordNet sense disambiguation tags.

Original languageAmerican English
Title of host publicationACL-08
Subtitle of host publicationHLT - 46th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies, Proceedings of the Conference
Pages227-235
Number of pages9
StatePublished - 2008
Event46th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies, ACL-08: HLT - Columbus, OH, United States
Duration: 15 Jun 200820 Jun 2008

Publication series

NameACL-08: HLT - 46th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies, Proceedings of the Conference

Conference

Conference46th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies, ACL-08: HLT
Country/TerritoryUnited States
CityColumbus, OH
Period15/06/0820/06/08

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