Fast on-line learning for multilingual categorization

DOIResolve DOI:
AuthorSearch for: ; Search for: ; Search for:
Proceedings titleSIGIR '12: Proceedings of the 35th international ACM SIGIR conference on Research and development in information retrieval
ConferenceSIGIR 2012 The 35th International ACM SIGIR conference on research and development in Information Retrieval, August 12-16, 2012, Portland, Oregon, USA
Pages10711072; # of pages: 2
Subjectmultilingual text categorisation; on-line learning
AbstractMultiview learning has been shown to be a natural and efficient framework for supervised or semi-supervised learning of multilingual document categorizers. The state-of-the-art co-regularization approach relies on alternate minimizations of a combination of language-specific categorization errors and a disagreement between the outputs of the monolingual text categorizers. This is typically solved by repeatedly training categorizers on each language with the appropriate regularizer. We extend and improve this approach by introducing an on-line learning scheme, where language-specific updates are interleaved in order to iteratively optimize the global cost in one pass. Our experimental results show that this produces similar performance as the batch approach, at a fraction of the computational cost.
Publication date
AffiliationInformation and Communication Technologies; National Research Council Canada
Peer reviewedYes
NPARC number21267663
Export citationExport as RIS
Report a correctionReport a correction
Record identifier36bd0934-6224-461c-a0b6-4200036db00f
Record created2013-03-26
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)