IMPROVING SEQUENCE TAGGING USING MACHINE-LEARNING TECHNIQUES
This paper presents an excel sequence tagging approach based on the combined machine learning methods. Firstly,conditional random fields (CRF) is presented as a new kind of discriminative sequential model, it can incorporate many rich features, and well avoid the label bias problem that is the limitation of maximum entropy Markov models (MEMM) and other discriminative finite-state models. Secondly, support vector machine is improved to adapt the sequential tagging task. Finally, these improved models and other existing models are combined together, which have achieved the state-of-the-art performance. Experimental results show that CRF approach achieves 0.70％ improvement in POS tagging and 0.67％ improvement in shallow parsing. Moreover, our combination method achieves F-measure 93.73％ and 93.69％in above two tasks respectively, which is better than any sub-model.
Conditional random fields Support vector machine Multi-model combination Sequence tagging
WEI JIANG XIAO-LONG WANG YI GUAN
School of Computer Science and Technology, Harbin Institute of Technology, 150001, Harbin, 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
2636-2641
2006-08-13（万方平台首次上网日期，不代表论文的发表时间）