Unsupervised constraint driven learning for transliteration discovery

Ming Wei Chang, Dan Goldwasser, Dan Roth, Yuancheng Tu

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

Abstract

This paper introduces a novel unsupervised constraint-driven learning algorithm for identifying named-entity (NE) transliterations in bilingual corpora. The proposed method does not require any annotated data or aligned corpora. Instead, it is bootstrapped using a simple resource - a romanization table. We show that this resource, when used in conjunction with constraints, can efficiently identify transliteration pairs. We evaluate the proposed method on transliterating English NEs to three different languages - Chinese, Russian and Hebrew. Our experiments show that constraint driven learning can significantly outperform existing unsupervised models and achieve competitive results to existing supervised models.

Original languageEnglish (US)
Title of host publicationNAACL HLT 2009 - Human Language Technologies
Subtitle of host publicationThe 2009 Annual Conference of the North American Chapter of the Association for Computational Linguistics, Proceedings of the Conference
Pages299-307
Number of pages9
StatePublished - 2009
EventHuman Language Technologies: The 2009 Annual Conference of the North American Chapter of the Association for Computational Linguistics, NAACL HLT 2009 - Boulder, CO, United States
Duration: May 31 2009Jun 5 2009

Other

OtherHuman Language Technologies: The 2009 Annual Conference of the North American Chapter of the Association for Computational Linguistics, NAACL HLT 2009
CountryUnited States
CityBoulder, CO
Period5/31/096/5/09

ASJC Scopus subject areas

  • Language and Linguistics
  • Social Sciences (miscellaneous)

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  • Cite this

    Chang, M. W., Goldwasser, D., Roth, D., & Tu, Y. (2009). Unsupervised constraint driven learning for transliteration discovery. In NAACL HLT 2009 - Human Language Technologies: The 2009 Annual Conference of the North American Chapter of the Association for Computational Linguistics, Proceedings of the Conference (pp. 299-307)