NDLI: Improving Sequence Tagging using Machine-Learning Techniques

Content Provider	IEEE Xplore Digital Library
Author	Wei Jiang Xiao-Long Wang Yi Guan
Copyright Year	2006
Description	Author affiliation: Sch. of Comput. Sci. & Technol., Harbin Inst. of Technol. (Wei Jiang; Xiao-Long Wang; Yi Guan)
Abstract	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
Sponsorship	IEEE Syst., Man and Cybernetics Hebei Univ.
Starting Page	2636
Ending Page	2641
File Size	250714
Page Count	6
File Format	PDF
ISBN	1424400619
DOI	10.1109/ICMLC.2006.258917
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2006-08-13
Publisher Place	China
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Tagging Hidden Markov models Support vector machines Entropy RNA Biological system modeling Computer science Learning systems Performance analysis Labeling Sequence tagging Conditional random fields Support vector machine Multi-model combination
Content Type	Text
Resource Type	Article

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