Cross-lingual Induction of Selectional Preferences with Bilingual Vector Spaces
Yves Peirsman and Sebastian Pado
In: NAACL 2010, Los Angeles(2010).
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.