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: Contribution to conferencePaperpeer-review

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
Pages623-629
Number of pages7
Publication statusPublished - 2010
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

ASJC Scopus subject areas

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

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