摘要： 针对Web信息抽取领域中存在的“项缺失”和“项无序”问题，提出一种基于隐马尔可夫模型的Web信息抽取方法。将Web文档解析为一棵扩展的DOM树，映射待抽取的信息项为状态，映射待抽取的信息项在扩展DOM树中的路径为词汇，使用归纳算法构造隐马尔可夫模型。实验结果证明该方法可以获得更好的抽取性能。

关键词: 信息抽取, 隐马尔可夫模型, 扩展DOM树

Abstract: To solve disorder among information items and lack of information item in the field of information extraction, this paper proposes a Web information extraction algorithm based on Hidden Markov Model(HMM). It parses a Web document into an extended DOM tree, and maps an information item to a state with mapping a path in extended DOM tree about an information item to a vocable. An HMM model is obtained by using induction algorithm. Experiments show that the algorithm has better extraction performance.

Key words: information extraction, Hidden Markov Model(HMM), extended DOM tree

中图分类号: 

• TP393