2003 NamedERtClassifierComb

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Subject Headings: Named Entity Recognition Algorithm, Language-Independent NER Algorithm, Language-Independent NER Task.

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Cited By

  • ~180 …

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Abstract

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.

References


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 AuthorvolumeDate ValuetitletypejournaltitleUrldoinoteyear
2003 NamedERtClassifierCombRadu Florian
Abe Ittycheriah
Hongyan Jing
Tong Zhang
Named Entity Recognition through Classifier CombinationProceedings of CoNLL-2003http://www.cnts.ua.ac.be/conll2003/pdf/16871flo.pdf2003