Discriminative learning for label sequences via boosting

Yasemin Altun, Thomas Hofmann, Mark Johnson

Research output: Chapter in Book/Report/Conference proceedingConference proceeding contributionpeer-review

22 Citations (Scopus)


This paper investigates a boosting approach to discriminative learning of label sequences based on a sequence rank loss function. The proposed method combines many of the advantages of boosting schemes with the efficiency of dynamic programming methods and is attractive both, conceptually and computationally. In addition, we also discuss alternative approaches based on the Hamming loss for label sequences. The sequence boosting algorithm offers an interesting alternative to methods based on HMMs and the more recently proposed Conditional Random Fields. Applications areas for the presented technique range from natural language processing and information extraction to computational biology. We include experiments on named entity recognition and part-of-speech tagging which demonstrate the validity and competitiveness of our approach.

Original languageEnglish
Title of host publicationAdvances in Neural Information Processing Systems 15 - Proceedings of the 2002 Conference, NIPS 2002
EditorsSuzanna Becker, Sebastian Thrun, Klaus Obermayer
Place of PublicationCambridge, MA
PublisherMIT Press
Number of pages8
ISBN (Print)0262025507, 9780262025508
Publication statusPublished - 2003
Externally publishedYes
Event16th Annual Neural Information Processing Systems Conference, NIPS - 2002 - Vancouver, Canada
Duration: 9 Dec 200214 Dec 2002


Other16th Annual Neural Information Processing Systems Conference, NIPS - 2002


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