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Volume 31 Issue 12
Dec.  2010
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Liu Peng-yuan, Zhao Tie-jun. Comparison of Web-Based Unsupervised Translation Disambiguation Word Model and N-gram Model[J]. Journal of Electronics & Information Technology, 2009, 31(12): 2969-2974. doi: 10.3724/SP.J.1146.2008.01624
Citation: Liu Peng-yuan, Zhao Tie-jun. Comparison of Web-Based Unsupervised Translation Disambiguation Word Model and N-gram Model[J]. Journal of Electronics & Information Technology, 2009, 31(12): 2969-2974. doi: 10.3724/SP.J.1146.2008.01624

Comparison of Web-Based Unsupervised Translation Disambiguation Word Model and N-gram Model

doi: 10.3724/SP.J.1146.2008.01624
  • Received Date: 2008-12-05
  • Rev Recd Date: 2009-05-07
  • Publish Date: 2009-12-19
  • This paper describes and compares web-based unsupervised translation disambiguation word model and N-gram model. For acquiring knowledge of disambiguation, both two models put differents queries to search engine and statistic page counts which it returned. Word model defines Web Bilingual Relatedness(WBR) between Chinese words and English words and disambiguates word sense by maxmizing Web Bilingual Relatedness between contexts and the translations of target word. Based on the hypothesis that the pattern of a polysemant is different while different sense of it is being used, N-gram model makes disambiguation by statisticing and analyzing N-grams of words in different semantic class of that polysemant. Both of the two models are evaluated on the SemEval2007 task#5, achieving the top performance against the state-of-the-art comparable unsupervised systems. Furthmore, N-gram model outperforms word model and the performence has potential for promotion when combine the results of that two class model.
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