Word Translation Disambiguation without Parallel Texts
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| Research areas: | Year: | 2011 | |||||
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| Type of Publication: | In Proceedings | Keywords: | word sense disambiguation, vector space models, n-gram language models | ||||
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| Book title: | Proceedings of the International Workshop on Using Linguistic Information for Hybrid Machine Translation | ||||||
| Address: | Barcelona, Spain | ||||||
| Organization: | LIHMT 2011 | Month: | November 18 | ||||
| ISBN: | 9788461529957 | ||||||
| Abstract: | Word Translation Disambiguation means to select the best translation(s) given a source word in context and a set of target candidates. Two approaches to determining similarity between input and sample context are presented, using n-gram and vector space models with huge annotated monolingual corpora as main knowledge source, rather than relying on large parallel corpora. Experiments on SemEval’s Cross-LingualWord Sense Disambiguation task (2010 English -German part) show some models on average surpassing the baselines, suggesting that translation disambiguation without parallel texts is feasible. |
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JRESEARCH_FULLTEXT:
 MarsiEtAl_WTD_2011.pdf | |||||||
