Co-related verb argument selectional preferences

Hiram Calvo, Kentaro Inui, Yuji Matsumoto

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


Learning Selectional Preferences has been approached as a verb and argument problem, or at most as a tri-nary relationship between subject, verb and object. The correlation of all arguments in a sentence, however, has not been extensively studied for sentence plausibility measuring because of the increased number of potential combinations and data sparseness. We propose a unified model for machine learning using SVM (Support Vector Machines) with features based on topic-projected words from a PLSI (Probabilistic Latent Semantic Indexing) Model and PMI (Pointwise Mutual Information) as co-occurrence features, and WordNet top concept projected words as semantic classes. We perform tests using a pseudo-disambiguation task. We found that considering all arguments in a sentence improves the correct identification of plausible sentences with an increase of 10% in recall among other things.

Original languageEnglish
Title of host publicationComputational Linguistics and Intelligent Text Processing - 12th International Conference, CICLing 2011, Proceedings
EditorsAlexander Gelbukh
PublisherSpringer Verlag
Number of pages11
ISBN (Print)9783642193996
Publication statusPublished - 2011
Event12th Annual Conference on Intelligent Text Processing and Computational Linguistics, CICLing 2011 - Tokyo, Japan
Duration: 2011 Feb 202011 Feb 26

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume6608 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349


Conference12th Annual Conference on Intelligent Text Processing and Computational Linguistics, CICLing 2011

ASJC Scopus subject areas

  • Theoretical Computer Science
  • Computer Science(all)


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