基于隐马尔可夫模型的Web信息抽取

Web Information Extraction Based on Hidden Markov Model

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。

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 ModeI（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.