Interpolated PLSI for learning plausible verb arguments

Hiram Calvo, Kentaro Inui, Yuji Matsumoto

Research output: Chapter in Book/Report/Conference proceedingConference contribution

1 Citation (Scopus)

Abstract

Learning Plausible Verb Arguments allows to automatically learn what kind of activities, where and how, are performed by classes of entities from sparse argument co-occurrences with a verb; this information it is useful for sentence reconstruction tasks. Calvo et al. (2009b) propose a non language-dependent model based on the Word Space Model for calculating the plausibility of candidate arguments given one verb and one argument, and compare with the single latent variable PLSI algorithm method, outperforming it. In this work we replicate their experiments with a different corpus, and explore variants to the PLSI method in order to explore further capabilities of this latter widely used technique. Particularly, we propose using an interpolated PLSI scheme that allows the combination of multiple latent semantic variables, and validate it in a task of identifying the real dependency-pair triple with regard to an artificially created one, obtaining up to 83% recall.

Original languageEnglish
Title of host publicationPACLIC 23 - Proceedings of the 23rd Pacific Asia Conference on Language, Information and Computation
Pages622-629
Number of pages8
Publication statusPublished - 2009 Dec 1
Externally publishedYes
Event23rd Pacific Asia Conference on Language, Information and Computation, PACLIC 23 - Hong Kong, China
Duration: 2009 Dec 32009 Dec 5

Publication series

NamePACLIC 23 - Proceedings of the 23rd Pacific Asia Conference on Language, Information and Computation
Volume2

Other

Other23rd Pacific Asia Conference on Language, Information and Computation, PACLIC 23
CountryChina
CityHong Kong
Period09/12/309/12/5

Keywords

  • Distributional thesaurus
  • K-nearest neighbors algorithm
  • KNN
  • PLSI
  • Plausible verb arguments
  • Probabilistic latent semantic indexing

ASJC Scopus subject areas

  • Language and Linguistics
  • Computer Science (miscellaneous)

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