document: Extracting semantic structure of web documents using content and visual information This work aims to provide a page segmentation algorithm which uses both visual and content information to extract the semantic structure of a web page . The visual information is utilized using the VIPS algorithm and the content information using a pre-trained Naive Bayes classifier . The output of the algorithm is a semantic structure tree whose leaves represent segments having unique topic . However contents of the leaf segments may possibly be physically distributed in the web page . This structure can be useful in many web applications like information retrieval , information extraction and automatic web page adaptation . This algorithm is expected to outperform other existing page segmentation algorithms since it utilizes both content and visual information .
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