Dependency language modeling using KNN and PLSI

Hiram Calvo, Kentaro Inui, Yuji Matsumoto

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

1 Citation (Scopus)

Abstract

In this paper we present a comparison of two language models based on dependency triples. We explore using the verb only for predicting the most plausible argument as in selectional preferences, as well as using both the verb and argument for predicting another argument. This latter causes a problem of data sparseness that must be solved by different techniques for data smoothing. Based on our results on the K-Nearest Neighbor model (KNN) algorithm we conclude that adding more information is useful for attaining higher precision, while the PLSI model was inconveniently sensitive to this information, yielding better results for the simpler model (using the verb only). Our results suggest that combining the strengths of both algorithms would provide best results.

Original languageEnglish
Title of host publicationMICAI 2009
Subtitle of host publicationAdvances in Artificial Intelligence - 8th Mexican International Conference on Artificial Intelligence, Proceedings
Pages136-144
Number of pages9
DOIs
Publication statusPublished - 2009 Dec 1
Externally publishedYes
Event8th Mexican International Conference on Artificial Intelligence, MICAI 2009 - Guanajuato, Mexico
Duration: 2009 Nov 92009 Nov 13

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume5845 LNAI
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Other

Other8th Mexican International Conference on Artificial Intelligence, MICAI 2009
CountryMexico
CityGuanajuato
Period09/11/909/11/13

ASJC Scopus subject areas

  • Theoretical Computer Science
  • Computer Science(all)

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