Improving feature extraction in named entity recognition based on maximum entropy model

Wei Jiang; Yi Guan; Xiao Long Wang

doi:10.1109/ICMLC.2006.258916

Improving feature extraction in named entity recognition based on maximum entropy model

Wei Jiang^*
, Yi Guan
, Xiao Long Wang

^*Corresponding author for this work

School of Computer Science and Technology, Harbin Institute of Technology

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

4 Scopus citations

Abstract

A new method of improving feature extraction for Named Entity Recognition is proposed in this paper. First of all, the context features and the entity features are extracted by the corresponding algorithm. The triggers extracted by Mutual Information, Information Gain, Average Mutual Information etc, are adopted to enhance the context features. And rough set theory is used to extract the entity features. Secondly, word cluster method is presented to improve the approach of expanding features, which make us select features more easily, and overcome the sparse data problem effectively. Finally, all the features are added into the maximum entropy model. The experiments have confirmed that our method is effective. The above method has been used in our word segmenter, which participated in the International SIGHAN-2005 Evaluation, and ranked first in open test in MSR corpus.

Original language	English
Title of host publication	Proceedings of the 2006 International Conference on Machine Learning and Cybernetics
Pages	2630-2635
Number of pages	6
DOIs	https://doi.org/10.1109/ICMLC.2006.258916
State	Published - 2006
Externally published	Yes
Event	2006 International Conference on Machine Learning and Cybernetics - Dalian, China Duration: 13 Aug 2006 → 16 Aug 2006

Publication series

Name	Proceedings of the 2006 International Conference on Machine Learning and Cybernetics
Volume	2006

Conference

Conference	2006 International Conference on Machine Learning and Cybernetics
Country/Territory	China
City	Dalian
Period	13/08/06 → 16/08/06

Keywords

Feature extraction
Maximum entropy model
Named entity recognition
Word cluster

Access to Document

10.1109/ICMLC.2006.258916

Cite this

Jiang, W., Guan, Y., & Wang, X. L. (2006). Improving feature extraction in named entity recognition based on maximum entropy model. In Proceedings of the 2006 International Conference on Machine Learning and Cybernetics (pp. 2630-2635). Article 4028507 (Proceedings of the 2006 International Conference on Machine Learning and Cybernetics; Vol. 2006). https://doi.org/10.1109/ICMLC.2006.258916

@inproceedings{74ecd5cc5a28492ab903892b7db18aff,

title = "Improving feature extraction in named entity recognition based on maximum entropy model",

abstract = "A new method of improving feature extraction for Named Entity Recognition is proposed in this paper. First of all, the context features and the entity features are extracted by the corresponding algorithm. The triggers extracted by Mutual Information, Information Gain, Average Mutual Information etc, are adopted to enhance the context features. And rough set theory is used to extract the entity features. Secondly, word cluster method is presented to improve the approach of expanding features, which make us select features more easily, and overcome the sparse data problem effectively. Finally, all the features are added into the maximum entropy model. The experiments have confirmed that our method is effective. The above method has been used in our word segmenter, which participated in the International SIGHAN-2005 Evaluation, and ranked first in open test in MSR corpus.",

keywords = "Feature extraction, Maximum entropy model, Named entity recognition, Word cluster",

author = "Wei Jiang and Yi Guan and Wang, \{Xiao Long\}",

year = "2006",

doi = "10.1109/ICMLC.2006.258916",

language = "英语",

isbn = "1424400619",

series = "Proceedings of the 2006 International Conference on Machine Learning and Cybernetics",

pages = "2630--2635",

booktitle = "Proceedings of the 2006 International Conference on Machine Learning and Cybernetics",

note = "2006 International Conference on Machine Learning and Cybernetics ; Conference date: 13-08-2006 Through 16-08-2006",

}

Jiang, W , Guan, Y & Wang, XL 2006, Improving feature extraction in named entity recognition based on maximum entropy model. in Proceedings of the 2006 International Conference on Machine Learning and Cybernetics., 4028507, Proceedings of the 2006 International Conference on Machine Learning and Cybernetics, vol. 2006, pp. 2630-2635, 2006 International Conference on Machine Learning and Cybernetics, Dalian, China, 13/08/06. https://doi.org/10.1109/ICMLC.2006.258916

Improving feature extraction in named entity recognition based on maximum entropy model. / Jiang, Wei ; Guan, Yi; Wang, Xiao Long.
Proceedings of the 2006 International Conference on Machine Learning and Cybernetics. 2006. p. 2630-2635 4028507 (Proceedings of the 2006 International Conference on Machine Learning and Cybernetics; Vol. 2006).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

TY - GEN

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AU - Jiang, Wei

AU - Guan, Yi

AU - Wang, Xiao Long

PY - 2006

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N2 - A new method of improving feature extraction for Named Entity Recognition is proposed in this paper. First of all, the context features and the entity features are extracted by the corresponding algorithm. The triggers extracted by Mutual Information, Information Gain, Average Mutual Information etc, are adopted to enhance the context features. And rough set theory is used to extract the entity features. Secondly, word cluster method is presented to improve the approach of expanding features, which make us select features more easily, and overcome the sparse data problem effectively. Finally, all the features are added into the maximum entropy model. The experiments have confirmed that our method is effective. The above method has been used in our word segmenter, which participated in the International SIGHAN-2005 Evaluation, and ranked first in open test in MSR corpus.

AB - A new method of improving feature extraction for Named Entity Recognition is proposed in this paper. First of all, the context features and the entity features are extracted by the corresponding algorithm. The triggers extracted by Mutual Information, Information Gain, Average Mutual Information etc, are adopted to enhance the context features. And rough set theory is used to extract the entity features. Secondly, word cluster method is presented to improve the approach of expanding features, which make us select features more easily, and overcome the sparse data problem effectively. Finally, all the features are added into the maximum entropy model. The experiments have confirmed that our method is effective. The above method has been used in our word segmenter, which participated in the International SIGHAN-2005 Evaluation, and ranked first in open test in MSR corpus.

KW - Feature extraction

KW - Maximum entropy model

KW - Named entity recognition

KW - Word cluster

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M3 - 会议稿件

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SN - 9781424400614

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BT - Proceedings of the 2006 International Conference on Machine Learning and Cybernetics

T2 - 2006 International Conference on Machine Learning and Cybernetics

Y2 - 13 August 2006 through 16 August 2006

ER -

Improving feature extraction in named entity recognition based on maximum entropy model

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