Multiway Tensor Factorization for Unsupervised Lexical Acquisition

Tim van de Cruys (1) , Laura Rimell (1) , Thierry Poibeau (2) , Anna Korhonen (3)

1 RCEAL - Research Centre for English and Applied Linguistics
2 Lattice - Lattice - Langues, Textes, Traitements informatiques, Cognition - UMR 8094
3 Computer Laboratory [Cambridge]

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.

Domaines: Traitement du texte et du document; Intelligence artificielle [cs.AI]; Informatique; Linguistique; Linguistique; Sciences de l'information et de la communication

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⟩