Training conditional random fields with multivariate evaluation measures

Jun Suzuki, Erik McDermott, Hideki Isozaki

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

41 Citations (Scopus)

Abstract

This paper proposes a framework for training Conditional Random Fields (CRFs) to optimize multivariate evaluation measures, including non-linear measures such as F-score. Our proposed framework is derived from an error minimization approach that provides a simple solution for directly optimizing any evaluation measure. Specifically focusing on sequential segmentation tasks, i.e. text chunking and named entity recognition, we introduce a loss function that closely reflects the target evaluation measure for these tasks, namely, segmentation F-score. Our experiments show that our method performs better than standard CRF training.

Original languageEnglish
Title of host publicationCOLING/ACL 2006 - 21st International Conference on Computational Linguistics and 44th Annual Meeting of the Association for Computational Linguistics, Proceedings of the Conference
PublisherAssociation for Computational Linguistics (ACL)
Pages217-224
Number of pages8
ISBN (Print)1932432655, 9781932432657
DOIs
Publication statusPublished - 2006
Externally publishedYes
Event21st International Conference on Computational Linguistics and 44th Annual Meeting of the Association for Computational Linguistics, COLING/ACL 2006 - Sydney, NSW, Australia
Duration: 2006 Jul 172006 Jul 21

Publication series

NameCOLING/ACL 2006 - 21st International Conference on Computational Linguistics and 44th Annual Meeting of the Association for Computational Linguistics, Proceedings of the Conference
Volume1

Other

Other21st International Conference on Computational Linguistics and 44th Annual Meeting of the Association for Computational Linguistics, COLING/ACL 2006
CountryAustralia
CitySydney, NSW
Period06/7/1706/7/21

ASJC Scopus subject areas

  • Language and Linguistics
  • Linguistics and Language

Fingerprint Dive into the research topics of 'Training conditional random fields with multivariate evaluation measures'. Together they form a unique fingerprint.

Cite this