We describe a cross-lingual method for the in- duction of selectional preferences for resource- poor languages, where no accurate monolin- gual 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 bilin- gual 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 monolin- gual baseline models.

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