Previous abstract | Contents | Next abstract

Named Entity Recognition through Classifier Combination

This paper presents a classifier-combination experimental framework for named entity recognition in which four diverse classifiers (robust linear classifier, maximum entropy, transformation-based learning, and hidden Markov model) are combined under different conditions. When no gazetteer or other additional training resources are used, the combined system attains a performance of 91.6F on the English development data; integrating name, location and person gazetteers, and named entity systems trained on additional, more general, data reduces the F-measure error by a factor of 15 to 21% on the English data.


Radu Florian, Abe Ittycheriah, Hongyan Jing and Tong Zhang, Named Entity Recognition through Classifier Combination. In: Proceedings of CoNLL-2003, Edmonton, Canada, 2003, pp. 168-171. [ps] [ps.gz] [pdf] [bibtex]
Last update: June 11, 2003. erikt@uia.ua.ac.be