Financial named entity recognition based on conditional random fields and information entropy

Shuwei Wang; Ruifeng Xu; Bin Liu; Lin Gui; Yu Zhou

doi:10.1109/ICMLC.2014.7009718

Financial named entity recognition based on conditional random fields and information entropy

Shuwei Wang, Ruifeng Xu^*, Bin Liu, Lin Gui, Yu Zhou

^*此作品的通讯作者

Harbin Institute of Technology

科研成果: 书/报告/会议事项章节 › 会议稿件 › 同行评审

16 引用（Scopus）

摘要

Named entity recognition plays an important role in many natural language processing tasks, such as relation detection and information extraction. This paper presents a novel method to recognize named entities infinancial news texts in three steps. First, the domain dictionary is applied to recognize stock names. Second, the full form FNEs are identified by incorporating internal features in a classifier based on Conditional Random Fields. Third, the mutual information, boundary entropy and context features are employed to recognize the abbreviation FNE candidates. The experiments completed on a Chinese financial dataset show that the proposed approach achieves 91.02% precision and 92.77% recall.

源语言	英语
主期刊名	Proceedings of 2014 International Conference on Machine Learning and Cybernetics, ICMLC 2014
出版商	IEEE Computer Society
页	838-843
页数	6
ISBN（电子版）	9781479942169
DOI	https://doi.org/10.1109/ICMLC.2014.7009718
出版状态	已出版 - 13 1月 2014
已对外发布	是
活动	13th International Conference on Machine Learning and Cybernetics, ICMLC 2014 - Lanzhou, 中国期限: 13 7月 2014 → 16 7月 2014

出版系列

姓名	Proceedings - International Conference on Machine Learning and Cybernetics
卷	2
ISSN（印刷版）	2160-133X
ISSN（电子版）	2160-1348

会议

会议	13th International Conference on Machine Learning and Cybernetics, ICMLC 2014
国家/地区	中国
市	Lanzhou
时期	13/07/14 → 16/07/14

访问文件

10.1109/ICMLC.2014.7009718

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引用此

Wang, S., Xu, R., Liu, B., Gui, L., & Zhou, Y. (2014). Financial named entity recognition based on conditional random fields and information entropy. 在 Proceedings of 2014 International Conference on Machine Learning and Cybernetics, ICMLC 2014 (页码 838-843). 文章 7009718 (Proceedings - International Conference on Machine Learning and Cybernetics; 卷 2). IEEE Computer Society. https://doi.org/10.1109/ICMLC.2014.7009718

@inproceedings{f6c96aabfb674f628f6661fb924d2f57,

title = "Financial named entity recognition based on conditional random fields and information entropy",

abstract = "Named entity recognition plays an important role in many natural language processing tasks, such as relation detection and information extraction. This paper presents a novel method to recognize named entities infinancial news texts in three steps. First, the domain dictionary is applied to recognize stock names. Second, the full form FNEs are identified by incorporating internal features in a classifier based on Conditional Random Fields. Third, the mutual information, boundary entropy and context features are employed to recognize the abbreviation FNE candidates. The experiments completed on a Chinese financial dataset show that the proposed approach achieves 91.02% precision and 92.77% recall.",

keywords = "Conditional Random Fields, Financial named entity, Information Entropy, Named entities recognition",

author = "Shuwei Wang and Ruifeng Xu and Bin Liu and Lin Gui and Yu Zhou",

note = "Publisher Copyright: {\textcopyright} 2014 IEEE.; 13th International Conference on Machine Learning and Cybernetics, ICMLC 2014 ; Conference date: 13-07-2014 Through 16-07-2014",

year = "2014",

month = jan,

day = "13",

doi = "10.1109/ICMLC.2014.7009718",

language = "English",

series = "Proceedings - International Conference on Machine Learning and Cybernetics",

publisher = "IEEE Computer Society",

pages = "838--843",

booktitle = "Proceedings of 2014 International Conference on Machine Learning and Cybernetics, ICMLC 2014",

address = "United States",

}

Wang, S, Xu, R, Liu, B, Gui, L & Zhou, Y 2014, Financial named entity recognition based on conditional random fields and information entropy. 在 Proceedings of 2014 International Conference on Machine Learning and Cybernetics, ICMLC 2014., 7009718, Proceedings - International Conference on Machine Learning and Cybernetics, 卷 2, IEEE Computer Society, 页码 838-843, 13th International Conference on Machine Learning and Cybernetics, ICMLC 2014, Lanzhou, 中国, 13/07/14. https://doi.org/10.1109/ICMLC.2014.7009718

Financial named entity recognition based on conditional random fields and information entropy. / Wang, Shuwei; Xu, Ruifeng; Liu, Bin 等.
Proceedings of 2014 International Conference on Machine Learning and Cybernetics, ICMLC 2014. IEEE Computer Society, 2014. 页码 838-843 7009718 (Proceedings - International Conference on Machine Learning and Cybernetics; 卷 2).

科研成果: 书/报告/会议事项章节 › 会议稿件 › 同行评审

TY - GEN

T1 - Financial named entity recognition based on conditional random fields and information entropy

AU - Wang, Shuwei

AU - Xu, Ruifeng

AU - Liu, Bin

AU - Gui, Lin

AU - Zhou, Yu

PY - 2014/1/13

Y1 - 2014/1/13

N2 - Named entity recognition plays an important role in many natural language processing tasks, such as relation detection and information extraction. This paper presents a novel method to recognize named entities infinancial news texts in three steps. First, the domain dictionary is applied to recognize stock names. Second, the full form FNEs are identified by incorporating internal features in a classifier based on Conditional Random Fields. Third, the mutual information, boundary entropy and context features are employed to recognize the abbreviation FNE candidates. The experiments completed on a Chinese financial dataset show that the proposed approach achieves 91.02% precision and 92.77% recall.

AB - Named entity recognition plays an important role in many natural language processing tasks, such as relation detection and information extraction. This paper presents a novel method to recognize named entities infinancial news texts in three steps. First, the domain dictionary is applied to recognize stock names. Second, the full form FNEs are identified by incorporating internal features in a classifier based on Conditional Random Fields. Third, the mutual information, boundary entropy and context features are employed to recognize the abbreviation FNE candidates. The experiments completed on a Chinese financial dataset show that the proposed approach achieves 91.02% precision and 92.77% recall.

KW - Conditional Random Fields

KW - Financial named entity

KW - Information Entropy

KW - Named entities recognition

UR - http://www.scopus.com/inward/record.url?scp=84921502704&partnerID=8YFLogxK

U2 - 10.1109/ICMLC.2014.7009718

DO - 10.1109/ICMLC.2014.7009718

M3 - Conference contribution

AN - SCOPUS:84921502704

T3 - Proceedings - International Conference on Machine Learning and Cybernetics

SP - 838

EP - 843

BT - Proceedings of 2014 International Conference on Machine Learning and Cybernetics, ICMLC 2014

PB - IEEE Computer Society

T2 - 13th International Conference on Machine Learning and Cybernetics, ICMLC 2014

Y2 - 13 July 2014 through 16 July 2014

ER -

Wang S, Xu R, Liu B, Gui L, Zhou Y. Financial named entity recognition based on conditional random fields and information entropy. 在 Proceedings of 2014 International Conference on Machine Learning and Cybernetics, ICMLC 2014. IEEE Computer Society. 2014. 页码 838-843. 7009718. (Proceedings - International Conference on Machine Learning and Cybernetics). doi: 10.1109/ICMLC.2014.7009718

Financial named entity recognition based on conditional random fields and information entropy

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