Improving Sequence Tagging using Machine-Learning Techniques
Wei Jiang, Xiaolong Wang, Yi Guan · 2006
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