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.
Complete list of metadatas

Cited literature [48 references]  Display  Hide  Download
Contributor : Thierry Poibeau <>
Submitted on : Tuesday, February 5, 2013 - 1:09:24 AM
Last modification on : Wednesday, May 22, 2019 - 3:46:02 PM
Long-term archiving on : Saturday, April 1, 2017 - 3:12:13 PM


Files produced by the author(s)


  • HAL Id : hal-00783711, version 1



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⟩



Record views


Files downloads