Guiding semi-supervision with constraint-driven learning

Ming Wei Chang, Lev Ratinov, Dan Roth

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

Abstract

Over the last few years, two of the main research directions in machine learning of natural language processing have been the study of semi-supervised learning algorithms as a way to train classifiers when the labeled data is scarce, and the study of ways to exploit knowledge and global information in structured learning tasks. In this paper, we suggest a method for incorporating domain knowledge in semi-supervised learning algorithms. Our novel framework unifies and can exploit several kinds of task specific constraints. The experimental results presented in the information extraction domain demonstrate that applying constraints helps the model to generate better feedback during learning, and hence the framework allows for high performance learning with significantly less training data than was possible before on these tasks.

Original languageEnglish (US)
Title of host publicationACL 2007 - Proceedings of the 45th Annual Meeting of the Association for Computational Linguistics
Pages280-287
Number of pages8
StatePublished - Dec 1 2007
Event45th Annual Meeting of the Association for Computational Linguistics, ACL 2007 - Prague, Czech Republic
Duration: Jun 23 2007Jun 30 2007

Publication series

NameACL 2007 - Proceedings of the 45th Annual Meeting of the Association for Computational Linguistics

Other

Other45th Annual Meeting of the Association for Computational Linguistics, ACL 2007
CountryCzech Republic
CityPrague
Period6/23/076/30/07

ASJC Scopus subject areas

  • Language and Linguistics
  • Linguistics and Language

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