A systematic cross-comparison of sequence classifiers

Binyamin Rozenfeld, Ronen Feldman, Moshe Fresko

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

4 Scopus citations


In the CoNLL 2003 NER shared task, more than two thirds of the submitted systems used a feature-rich representation of the task. Most of them used the maximum entropy principle to combine the features together. Others used large margin linear classifiers, such as SVM and RRM. In this paper, we compare several common classifiers under exactly the same conditions, demonstrating that the ranking of systems in the shared task is due to feature selection and other causes and not due to inherent qualities of the algorithms, which should be ranked otherwise. We demonstrate that whole-sequence models generally outperform local models, and that large margin classifiers generally outperform maximum entropy-based classifiers.

Original languageEnglish
Title of host publicationProceedings of the Sixth SIAM International Conference on Data Mining
PublisherSociety for Industrial and Applied Mathematics
Number of pages5
ISBN (Print)089871611X, 9780898716115
StatePublished - 2006
EventSixth SIAM International Conference on Data Mining - Bethesda, MD, United States
Duration: 20 Apr 200622 Apr 2006

Publication series

NameProceedings of the Sixth SIAM International Conference on Data Mining


ConferenceSixth SIAM International Conference on Data Mining
Country/TerritoryUnited States
CityBethesda, MD


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