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We describe a cross-lingual method for the induction of selectional preferences for resource-poor languages, where no accurate monolingual models are available. The method uses bilingual vector spaces to “translate” foreign language predicate-argument structures into a resource-rich language like English. The only prerequisite for constructing the bilingual vector space is a large unparsed corpus in the resource-poor language, although the model can profit from (even noisy) syntactic knowledge. Our experiments show that the cross-lingual predictions correlate well with human ratings, clearly outperforming monolingual baseline models. ispartof: pages:1-9 ispartof: Proceedings of Human Language Technologies: The 11th Annual Conference of the North American Chapter of the Association for Computational Linguistics pages:1-9 ispartof: NAACL location:Los Angeles date:1 Jun - 6 Jun 2010 status: published |
