An extension of the aspect PLSA model to active and semi-supervised learning for text classification

  1. (PDF, 364 KB)
DOIResolve DOI:
AuthorSearch for: ; Search for: ; Search for: ; Search for:
Proceedings titleArtificial Intelligence
Series titleLecture Notes in Computer Science; Volume 6040
ConferenceSETN-2010: 6th Hellenic Conference on Artificial Intelligence, 4-7 May 2010, Athens, Greece
Pages183192; # of pages: 10
AbstractIn this paper, we address the problem of learning aspect models with partially labeled examples. We propose a method which benefits from both semi-supervised and active learning frameworks. In particular, we combine a semi-supervised extension of the PLSA algorithm with two active learning techniques. We perform experiments over four different datasets and show the effectiveness of the combination of the two frameworks.
Publication date
AffiliationNRC Institute for Information Technology; National ResearchCouncil Canada
Peer reviewedYes
NPARC number16907873
Export citationExport as RIS
Report a correctionReport a correction
Record identifierf2c2f8f4-bb07-4f30-bcba-064c39ff0268
Record created2011-02-22
Record modified2016-05-09
Bookmark and share
  • Share this page with Facebook (Opens in a new window)
  • Share this page with Twitter (Opens in a new window)
  • Share this page with Google+ (Opens in a new window)
  • Share this page with Delicious (Opens in a new window)
Date modified: