Application Study of Hidden Markov Model and Maximum Entropy in Text Information Extraction

Authors:
Rong Li;Li-Ying Liu;He-Fang Fu;Jia-Heng Zheng
Affiliations:
Department of computer, Xinzhou Teachers' College, Xinzhou, China 034000;Weather bureau of Xinzhou City, Xinzhou, China 034000;Department of computer, Xinzhou Teachers' College, Xinzhou, China 034000;School of Computer and Information Technology, Shanxi University, Taiyuan, China 030006
Venue:
AICI '09 Proceedings of the International Conference on Artificial Intelligence and Computational Intelligence
Year:
2009

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Abstract

Text information extraction is an important approach to process large quantity of text. Since the traditional training method of hidden Markov model for text information extraction is sensitive to initial model parameters and easy to converge to a local optimal model in practice, a novel algorithm using hidden Markov model based on maximal entropy for text information extraction is presented. The new algorithm combines the advantage of maximum entropy model, which can integrate and process rules and knowledge efficiently, with that of hidden Markov model, which has powerful technique foundations to solve sequence representation and statistical problem. And the algorithm uses the sum of all features with weights to adjust the transition parameters in hidden Markov model for text information extraction. Experimental results show that compared with the simple hidden Markov model, the new algorithm improves the performance in precision and recall.