Learning co-relations of plausible verb arguments with a WSM and a distributional thesaurus

Hiram Calvo, Kentaro Inui, Yuji Matsumoto

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

Abstract

We propose a model based on the Word Space Model for calculating the plausibility of candidate arguments given one verb and one argument. The resulting information can be used in co-reference resolution, zero-pronoun resolution or syntactic ambiguity tasks. Previous work such as Selectional Preferences or Semantic Frames acquisition focuses on this task using supervised resources, or predicting arguments independently from each other. On this work we explore the extraction of plausible arguments considering their co-relation, and using no more information than that provided by the dependency parser. This creates a data sparseness problem alleviated by using a distributional thesaurus built from the same data for smoothing. We compare our model with the traditional PLSI method.

Original languageEnglish
Title of host publicationProgress in Pattern Recognition, Image Analysis, Computer Vision and Applications - 14th Iberoamerican Conference on Pattern Recognition, CIARP 2009, Proceedings
Pages363-370
Number of pages8
DOIs
Publication statusPublished - 2009
Event14th Iberoamerican Conference on Pattern Recognition, CIARP 2009 - Guadalajara, Jalisco, Mexico
Duration: 2009 Nov 152009 Nov 18

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume5856 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Other

Other14th Iberoamerican Conference on Pattern Recognition, CIARP 2009
CountryMexico
CityGuadalajara, Jalisco
Period09/11/1509/11/18

ASJC Scopus subject areas

  • Theoretical Computer Science
  • Computer Science(all)

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