Abstract
The traditional Hidden Markov Model for web information extraction is sensitive to the initial model parameters and easy to lead to a sub-optimal model in practice. A hybrid conditional model to combine maximum entropy and maximum entropy Markov model is put forward for Web information extraction. With this approach, the input Web page is parsed to build an HTML tree, data regions are located in each HTML sub-tree node by estimating the entropy, which allows observations to be represented as arbitrary overlapping features (such as vocabulary, capitalization, HTML tags, and semantics), and defines the conditional probability of state sequences given to observation sequences for Web information extraction. Experimental results show that the new approach improves the performance in precision and recall over traditional hidden Markov model and maximum entropy Markov model.
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