A post-processing approach to statistical word alignment reflecting alignment tendency between part-of-speeches

Jae Hee Lee, Seung Wook Lee, Gumwon Hong, Young Sook Hwang, Sang Bum Kim, Hae-Chang Rim

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

2 Citations (Scopus)

Abstract

Statistical word alignment often suffers from data sparseness. Part-of-speeches are often incorporated in NLP tasks to reduce data sparseness. In this paper, we attempt to mitigate such problem by reflecting alignment tendency between part-of-speeches to statistical word alignment. Because our approach does not rely on any language-dependent knowledge, it is very simple and purely statistic to be applied to any language pairs. End-to-end evaluation shows that the proposed method can improve not only the quality of statistical word alignment but the performance of statistical machine translation.

Original languageEnglish
Title of host publicationColing 2010 - 23rd International Conference on Computational Linguistics, Proceedings of the Conference
Pages623-629
Number of pages7
Volume2
Publication statusPublished - 2010 Dec 1
Event23rd International Conference on Computational Linguistics, Coling 2010 - Beijing, China
Duration: 2010 Aug 232010 Aug 27

Other

Other23rd International Conference on Computational Linguistics, Coling 2010
CountryChina
CityBeijing
Period10/8/2310/8/27

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ASJC Scopus subject areas

  • Language and Linguistics
  • Computational Theory and Mathematics
  • Linguistics and Language

Cite this

Lee, J. H., Lee, S. W., Hong, G., Hwang, Y. S., Kim, S. B., & Rim, H-C. (2010). A post-processing approach to statistical word alignment reflecting alignment tendency between part-of-speeches. In Coling 2010 - 23rd International Conference on Computational Linguistics, Proceedings of the Conference (Vol. 2, pp. 623-629)