Multiway Tensor Factorization for Unsupervised Lexical Acquisition

Abstract : This paper introduces a novel method for joint unsupervised aquisition of verb subcategorization frame (SCF) and selectional preference (SP) information. Treating SCF and SP induction as a multi-way co-occurrence problem, we use multi-way tensor factorization to cluster frequent verbs from a large corpus according to their syntactic and semantic behaviour. The method extends previous tensor factorization approaches by predicting whether a syntactic argument is likely to occur with a verb lemma (SCF) as well as which lexical items are likely to occur in the argument slot (SP), and integrates a variety of lexical and syntactic features, including co-occurrence information on grammatical relations not explicitly represented in the SCFs. The SCF lexicon that emerges from the clusters achieves an F-score of 68.7 against a gold standard, while the SP model achieves an accuracy of 77.8 in a novel evaluation that considers all of a verb's arguments simultaneously.
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Tim van de Cruys, Laura Rimell, Thierry Poibeau, Anna Korhonen. Multiway Tensor Factorization for Unsupervised Lexical Acquisition. COLING 2012, Dec 2012, Mumbai, India. pp.2703-2720. ⟨hal-00783711⟩

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