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 | ||||
Authors: |
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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
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