Regularizing structured classifier with conditional probabilistic constraints for semi-supervised learning

Vincent W. Zheng, Kevin Chen Chuan Chang

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

Abstract

Constraints have been shown as an effective way to incorporate unlabeled data for semi-supervised structured classification. We recognize that, constraints are often conditional and probabilistic; moreover, a constraint can have its condition depend on either just observations (which we call x-type constraint) or even hidden variables (which we call y-type constraint). We wish to design a constraint formulation that can flexibly model the constraint probability for both x-type and y-type constraints, and later use it to regularize general structured classifiers for semi-supervision. Surprisingly, none of the existing models have such a constraint formulation. Thus in this paper, we propose a new conditional probabilistic formulation for modeling both x-type and y-type constraints. We also recognize the inference complication for y-type constraint, and propose a systematic selective evaluation approach to efficiently realize the constraints. Finally, we evaluate our model in three applications, including named entity recognition, part-of-speech tagging and entity information extraction, with totally nine data sets. We show that our model is generally more accurate and efficient than the state-of-the-art baselines. Our code and data are available at https://bitbucket.org/vwz/cikm2016-cpf/.

Original languageEnglish (US)
Title of host publicationCIKM 2016 - Proceedings of the 2016 ACM Conference on Information and Knowledge Management
PublisherAssociation for Computing Machinery
Pages1029-1038
Number of pages10
ISBN (Electronic)9781450340731
DOIs
StatePublished - Oct 24 2016
Event25th ACM International Conference on Information and Knowledge Management, CIKM 2016 - Indianapolis, United States
Duration: Oct 24 2016Oct 28 2016

Publication series

NameInternational Conference on Information and Knowledge Management, Proceedings
Volume24-28-October-2016

Other

Other25th ACM International Conference on Information and Knowledge Management, CIKM 2016
CountryUnited States
CityIndianapolis
Period10/24/1610/28/16

Keywords

  • Conditional probabilistic constraint
  • Structured classifier

ASJC Scopus subject areas

  • Business, Management and Accounting(all)
  • Decision Sciences(all)

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  • Cite this

    Zheng, V. W., & Chang, K. C. C. (2016). Regularizing structured classifier with conditional probabilistic constraints for semi-supervised learning. In CIKM 2016 - Proceedings of the 2016 ACM Conference on Information and Knowledge Management (pp. 1029-1038). (International Conference on Information and Knowledge Management, Proceedings; Vol. 24-28-October-2016). Association for Computing Machinery. https://doi.org/10.1145/2983323.2983860