Named entity recognition in Vietnamese using classifier voting

Authors:
Pham Thi Xuan Thao;Tran Quoc Tri;Dinh Dien;Nigel Collier
Affiliations:
University of Natural Sciences, VNU of HCMC;University of Natural Sciences, VNU of HCMC;University of Natural Sciences, VNU of HCMC;National Institute of Informatics
Venue:
ACM Transactions on Asian Language Information Processing (TALIP)
Year:
2007

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Abstract

Named entity recognition (NER) is one of the fundamental tasks in natural-language processing (NLP). Though the combination of different classifiers has been widely applied in several well-studied languages, this is the first time this method has been applied to Vietnamese. In this article, we describe how voting techniques can improve the performance of Vietnamese NER. By combining several state-of-the-art machine-learning algorithms using voting strategies, our final result outperforms individual algorithms and gained an F-measure of 89.12. A detailed discussion about the challenges of NER in Vietnamese is also presented.