Abstract：According to the structure and the content features of web pages, a model named tree-structured hierarchical conditional random fields (TH-CRFs) is proposed. Firstly, a multi-feature vector space model is proposed to represent the features of the web pages from the facets of the page structure and the content. Secondly, the Boolean model and multi-rules are introduced to denote the features for a better representation of the web objects. Thirdly, an optimal web objects information extraction based on the TH-CRFs is performed to find out the recruitment knowledge and optimize the efficiency of the training. Finally, the proposed model is compared with the existing approaches for web objects information extraction. The experimental results show that the accuracy of the TH-CRFs for the web objects information extraction is significantly improved, and the time complexity is decreased.
王静，刘志镜. 基于概率模型的Web信息抽取[J]. 模式识别与人工智能, 2010, 23(6): 847-855.
WANG Jing,LIU Zhi-Jing. Web Information Extraction Based on Probabilistic Model. , 2010, 23(6): 847-855.
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