Rupesh R. Mehta
ABSTRACT
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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Digital Library
- J.-R. W. D. Cai, Yu, S., W.-Y. Ma., Extracting content structure for web pages based on visual representation. Proc. 5th Asia Pacific Web Conf, Xi'an, China, 2003. Google ScholarDigital Library
- Mitchell, T., Machine Learning, McGraw-Hill, NY, 1997. Google ScholarDigital Library
- Open directory project. http://dmoz.org/.Google Scholar
Index Terms
- Extracting semantic structure of web documents using content and visual information
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