Paper: Named Entity Recognition Through Classifier Combination

ACL ID W03-0425
Title Named Entity Recognition Through Classifier Combination
Venue International Conference on Computational Natural Language Learning
Session Main Conference
Year 2003

This paper presents a classifier-combination experimental framework for named entity recognition in which four diverse classi- fiers (robust linear classifier, maximum en- tropy, transformation-based learning, and hid- den Markov model) are combined under differ- ent conditions. When no gazetteer or other ad- ditional training resources are used, the com- bined system attains a performance of 91.6F on the English development data; integrat- ing 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.