Named entity recogintion through classifier combination

Radu Florian; Abe Ittycheriah; Hongyan Jing; Tong Zhang

doi:10.3115/1119176.1119201

Named entity recogintion through classifier combination

Radu Florian, Abe Ittycheriah, Hongyan Jing, Tong Zhang

Research output: Contribution to conference › Conference Paper › peer-review

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.

Original language	English
Pages	168-171
DOIs	https://doi.org/10.3115/1119176.1119201
Publication status	Published - 2003
Externally published	Yes
Event	Proceedings of the seventh conference on Natural language learning, CONLL 2003 - Duration: 1 Jan 2003 → 1 Jan 2003

Conference

Conference	Proceedings of the seventh conference on Natural language learning, CONLL 2003
Period	1/01/03 → 1/01/03

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10.3115/1119176.1119201

Cite this

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title = "Named entity recogintion through classifier combination",

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.",

author = "Radu Florian and Abe Ittycheriah and Hongyan Jing and Tong Zhang",

year = "2003",

doi = "10.3115/1119176.1119201",

language = "English",

pages = "168--171",

note = "Proceedings of the seventh conference on Natural language learning, CONLL 2003 ; Conference date: 01-01-2003 Through 01-01-2003",

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AU - Florian, Radu

AU - Ittycheriah, Abe

AU - Jing, Hongyan

AU - Zhang, Tong

PY - 2003

Y1 - 2003

N2 - 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.

AB - 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.

UR - https://openalex.org/W2056451646

U2 - 10.3115/1119176.1119201

DO - 10.3115/1119176.1119201

M3 - Conference Paper

SP - 168

EP - 171

T2 - Proceedings of the seventh conference on Natural language learning, CONLL 2003

Y2 - 1 January 2003 through 1 January 2003

ER -

Named entity recogintion through classifier combination

Abstract

Conference

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