Application of Symbol Feature-Based HMM in Web Information Extraction
Ma Yongjin, Jin Bingyao · 2009
This paper proposes a symbol feature-based hidden Markov model (HMM). Each state in the model is expressed by some symbol features, and is described by feature lists that draw from regular expressions and text inference; based on which, we use Viterbi Algorithm to extract the information from scientific researcherspsila homepages. It works well although there is great information redundancy.