Similarity-Based Models of Word Cooccurrence Probabilities
Machine Learning - Special issue on natural language learning
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Cross-lingual induction of selectional preferences with bilingual vector spaces
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Bilingual lexicons, essential to many NLP applications, can be constructed automatically on the basis of parallel or comparable corpora. In this article, we make two contributions to their induction from comparable corpora. The first one concerns the creation of these lexicons. We show that seed lexicons can be improved by adding a bootstrapping procedure that uses cross-lingual distributional similarity. The second contribution concerns the evaluation of bilingual lexicons. It is generally based on translation lexicons, which corresponds to the implicit assumption that (cross-lingual) synonymy is the semantic relation of primary interest, even though other semantic relations like (cross-lingual) hyponymy or cohyponymy make up a considerable portion of translation pair candidates proposed by distributional methods. We argue that the focus on synonymy is an oversimplification and that many applications can profit from the inclusion of other semantic relations. We study what effect these semantic relations have on two cross-lingual tasks: the cross-lingual projection of polarity scores and the cross-lingual modeling of selectional preferences. We find that the presence of non-synonymous semantic relations may negatively affect the former of these tasks, but benefit the latter.