DocumentCode :
1798468
Title :
Financial named entity recognition based on conditional random fields and information entropy
Author :
Shuwei Wang ; Ruifeng Xu ; Bin Liu ; Lin Gui ; Yu Zhou
Author_Institution :
Shenzhen Grad. Sch., Harbin Inst. of Technol., Shenzhen, China
Volume :
2
fYear :
2014
fDate :
13-16 July 2014
Firstpage :
838
Lastpage :
843
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 in financial 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 :
entropy; financial data processing; natural language processing; pattern classification; stock markets; text analysis; Chinese financial dataset; abbreviation FNE candidates; boundary entropy; conditional random fields; context features; domain dictionary; financial named entity recognition; financial news texts; full form FNEs; information entropy; mutual information; natural language processing tasks; stock name recognition; Abstracts; Classification algorithms; Dynamic programming; Conditional Random Fields; Financial named entity; Information Entropy; Named entities recognition;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Machine Learning and Cybernetics (ICMLC), 2014 International Conference on
Conference_Location :
Lanzhou
ISSN :
2160-133X
Print_ISBN :
978-1-4799-4216-9
Type :
conf
DOI :
10.1109/ICMLC.2014.7009718
Filename :
7009718
Link To Document :
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