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.
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Li, R., Liu, Ly., Fu, Hf., Zheng, Jh. (2009). Application Study of Hidden Markov Model and Maximum Entropy in Text Information Extraction. In: Deng, H., Wang, L., Wang, F.L., Lei, J. (eds) Artificial Intelligence and Computational Intelligence. AICI 2009. Lecture Notes in Computer Science(), vol 5855. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-05253-8_44
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DOI: https://doi.org/10.1007/978-3-642-05253-8_44
Publisher Name: Springer, Berlin, Heidelberg
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