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

科研成果: 书/报告/会议事项章节会议稿件同行评审

15 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
838-843
页数6
ISBN(电子版)9781479942169
DOI
出版状态已出版 - 13 1月 2014
已对外发布
活动13th International Conference on Machine Learning and Cybernetics, ICMLC 2014 - Lanzhou, 中国
期限: 13 7月 201416 7月 2014

出版系列

姓名Proceedings - International Conference on Machine Learning and Cybernetics
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
Lanzhou
时期13/07/1416/07/14

指纹

探究 'Financial named entity recognition based on conditional random fields and information entropy' 的科研主题。它们共同构成独一无二的指纹。

引用此