A hybrid approach for web information extraction

This paper presents a new approach based on maximum entropy and maximum entropy Markov model for web information extraction. This approach is not only able to overcome the shortcoming of the less precision and recall of the hidden Markov model. In addition, this approach can make the most of various kinds of contextual information from web. The experiments are found that the hybrid approach has an average precision rate of 87.516% while the hidden Markov model trained by the Baum-Welch algorithm has an average precision rate of 68.630%. This implies that the hybrid approach is more optimized than the hidden Markov model trained by the Baum-Welch algorithm.