Learning ensembles of structured prediction rules

Corinna Cortes, Vitaly Kuznetsov, Mehryar Mohri

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

Abstract

We present a series of algorithms with theoretical guarantees for learning accurate ensembles of several structured prediction rules for which no prior knowledge is assumed. This includes a number of randomized and deterministic algorithms devised by converting on-line learning algorithms to batch ones, and a boostingstyle algorithm applicable in the context of structured prediction with a large number of labels. We also report the results of extensive experiments with these algorithms.

Original languageEnglish (US)
Title of host publicationLong Papers
PublisherAssociation for Computational Linguistics (ACL)
Pages1-12
Number of pages12
ISBN (Print)9781937284725
DOIs
StatePublished - 2014
Event52nd Annual Meeting of the Association for Computational Linguistics, ACL 2014 - Baltimore, MD, United States
Duration: Jun 22 2014Jun 27 2014

Publication series

Name52nd Annual Meeting of the Association for Computational Linguistics, ACL 2014 - Proceedings of the Conference
Volume1

Other

Other52nd Annual Meeting of the Association for Computational Linguistics, ACL 2014
CountryUnited States
CityBaltimore, MD
Period6/22/146/27/14

ASJC Scopus subject areas

  • Language and Linguistics
  • Linguistics and Language

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  • Cite this

    Cortes, C., Kuznetsov, V., & Mohri, M. (2014). Learning ensembles of structured prediction rules. In Long Papers (pp. 1-12). (52nd Annual Meeting of the Association for Computational Linguistics, ACL 2014 - Proceedings of the Conference; Vol. 1). Association for Computational Linguistics (ACL). https://doi.org/10.3115/v1/p14-1001