Understanding relation temporality of entities

Taesung Lee, Seung Won Hwang

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

Abstract

This paper demonstrates the importance of relation equivalence for entity translation pair discovery. Existing approach of understanding relation equivalence has focused on using explicit features of cooccurring entities. In this paper, we explore latent features of temporality for understanding relation equivalence, and empirically show that the explicit and latent features complement each other. Our proposed hybrid approach of using both explicit and latent features improves relation translation by 0.16 F1-score, and in turn improves entity translation by 0.02.

Original languageEnglish
Title of host publicationLong Papers
PublisherAssociation for Computational Linguistics (ACL)
Pages848-853
Number of pages6
ISBN (Print)9781937284732
DOIs
Publication statusPublished - 2014
Event52nd Annual Meeting of the Association for Computational Linguistics, ACL 2014 - Baltimore, MD, United States
Duration: 2014 Jun 222014 Jun 27

Publication series

Name52nd Annual Meeting of the Association for Computational Linguistics, ACL 2014 - Proceedings of the Conference
Volume2

Other

Other52nd Annual Meeting of the Association for Computational Linguistics, ACL 2014
CountryUnited States
CityBaltimore, MD
Period14/6/2214/6/27

All Science Journal Classification (ASJC) codes

  • Language and Linguistics
  • Linguistics and Language

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