基于HMM的Web信息抽取算法的研究与应用

Improvement of Web Information Extraction Algorithm Based on HMM

随着因特网技术的迅速发展,网上信息成几何级数增长,如何从这些海量联机非结构化文本中自动抽取出结构化信息成为目前重要的研究课题。研究了基于隐马尔可夫模型的Web信息抽取算法,着重探讨了隐马尔可夫模型在文本信息抽取中应该如何应用,数据应该如何标记,并对隐马尔可夫模型在文本信息抽取中的应用提出了几个改进的方法,建立了基于HMM的Web信息抽取模型,并对信息抽取后的数据进行了分析对比,验证了改进算法的有效性。

With thc development of the Internet technologies,the information on the Internet increases exponentially. One important research focuses on how to extract structured data from these great capacities of online documents in un- structured texts. This thesis mainly studied relative algorithms on Web information extraction based on hidden Markov model（HMM）,discussed how to use HMM and how to mark data in text information extraction, offered several metho- ds to improve the hidden Markov model in information extraction,introduced the establishment of Web information ex traction model based on HMM, Comparatively analysed the output data of information extraction, verified the validity of the algorithm through experiments.