Online ISSN:1349-8606
Progress in Informatics  
No.4 March 2007  
Page 5-13 PDF(1,453KB) | References
Named Entity Recognition in Vietnamese documents
Tri Tran Q.1, Thao Pham T. X.2, Hung Ngo Q.3, Dien DINH4 and Nigel COLLIER5
1,2,3Faculty of Computer Sciences, University of Information Technology-VNU of HCMC
4Faculty of Information Technology, University of Natural Sciences - VNU of HCMC
5National Institute of Informatics
(Received: October 30, 2006)
(Revised: January 16, 2007)
(Accepted: January 17, 2007)
Named Entity Recognition (NER) aims to classify words in a document into pre-defined target entity classes and is now considered to be fundamental for many natural language processing tasks such as information retrieval, machine translation, information extraction and question answering. This paper presents the results of an experiment in which a Support Vector Machine (SVM) based NER model is applied to the Vietnamese language. Though this state of the art machine learning method has been widely applied to NER in several well-studied languages, this is the first time this method has been applied to Vietnamese. In a comparison against Conditional Random Fields (CRFs) the SVM model was shown to outperform CRF by optimizing its feature window size, obtaining an overall F-score of 87.75. The paper also presents a detailed discussion about the characteristics of the Vietnamese language and provides an analysis of the factors which influence performance in this task.
Named Entity Recognition (NER), Support Vector Machine (SVM), text mining
PDF(1,453KB) | References

National Institute of Informatics is a member of CrossRef.
Go back HOME