Named entity translation with web mining and transliteration

  • Authors:
  • Long Jiang;Ming Zhou;Lee-Feng Chien;Cheng Niu

  • Affiliations:
  • Microsoft Research Asia, Haidian District, Beijing, PRC;Microsoft Research Asia, Haidian District, Beijing, PRC;Institute of Information Science, Academia Sinica, Taiwan;Microsoft Research Asia, Haidian District, Beijing, PRC

  • Venue:
  • IJCAI'07 Proceedings of the 20th international joint conference on Artifical intelligence
  • Year:
  • 2007

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Abstract

This paper presents a novel approach to improve the named entity translation by combining a transliteration approach with web mining, using web information as a source to complement transliteration, and using transliteration information to guide and enhance web mining. A Maximum Entropy model is employed to rank translation candidates by combining pronunciation similarity and bilingual contextual co-occurrence. Experimental results show that our approach effectively improves the precision and recall of the named entity translation by a large margin.