Collaborative entity extraction and translation

Heng Ji, Ralph Grishman

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

Abstract

Entity extraction is the task of identifying names and nominal phrases ('mentions') in a text and linking coreferring mentions. We propose the use of a new source of data for improving entity extraction: the information gleaned from large bitexts and captured by a statistical, phrase-based machine translation system. We translate the individual mentions and test properties of the translated mentions, as well as comparing the translations of coreferring mentions. The results provide feedback to improve source language entity extraction. Experiments on Chinese and English show that this approach can significantly improve Chinese entity extraction (2.2%-relative improvement in name tagging F-measure, representing a 15.0% error reduction), as well as Chinese to English entity translation (9.1% relative improvement in F-measure), over state-of-the-art entity extraction and machine translation systems.

Original languageEnglish (US)
Title of host publicationInternational Conference Recent Advances in Natural Language Processing, RANLP 2007 - Proceedings
EditorsNicolas Nicolov, Nikolai Nikolov, Ruslan Mitkov, Kalina Bontcheva, Galia Angelova
PublisherAssociation for Computational Linguistics (ACL)
Pages303-309
Number of pages7
ISBN (Electronic)9789549174373
StatePublished - 2007
Externally publishedYes
EventInternational Conference Recent Advances in Natural Language Processing, RANLP 2007 - Borovets, Bulgaria
Duration: Sep 27 2007Sep 29 2007

Publication series

NameInternational Conference Recent Advances in Natural Language Processing, RANLP
Volume2007-January
ISSN (Print)1313-8502

Conference

ConferenceInternational Conference Recent Advances in Natural Language Processing, RANLP 2007
Country/TerritoryBulgaria
CityBorovets
Period9/27/079/29/07

Keywords

  • Joint inference
  • Machine translation
  • Named entities

ASJC Scopus subject areas

  • Software
  • Computer Science Applications
  • Artificial Intelligence
  • Electrical and Electronic Engineering

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