CHINESE NAMED ENTITY RECOGNITION USING SUPPORT VECTOR MACHINES
Named entity recognition (NER) is low-level semantics technology. Since it is simple and efficient, it has been widely applied in many systems such as machine translation,information retrieval, information extraction, question answering and summarization. The goal of named entity recognition is to classify names into some particular categories from text, such as the names of people, places, and organizations. Previous studies focus on combining abundant rules or trigger words to enhance the system performance.These methods require domain experts to build up the rules and word set. In this paper, we present a robust named entity recognition system based on support vector machines (SVM).In the experiment, we perform the One-against-One SVM algorithm and a feature extraction method to achieve high accuracy.
Named entity recognition information extraction Support Vector Machines
XU-DONG LIN HONG PENG BO LIU
College of Computer Science and Engineering, South China University of Technology, Guangzhou 510640 P.R.China
国际会议
2006 International Conference on Machine Learning and Cybernetics(IEEE第五届机器学习与控制论坛)
大连
英文
4216-4220
2006-08-13（万方平台首次上网日期，不代表论文的发表时间）