document: Webpage understanding : an integrated approach Recent work has shown the effectiveness of leveraging layout and tag-tree structure for segmenting webpages and labeling HTML elements . However , how to effectively segment and label the text contents inside HTML elements is still an open problem . Since many text contents on a webpage are often text fragments and not strictly grammatical , traditional natural language processing techniques , that typically expect grammatical sentences , are no longer directly applicable . In this paper , we examine how to use layout and tag-tree structure in a principled way to help understand text contents on webpages . We propose to segment and label the page structure and the text content of a webpage in a joint discriminative probabilistic model . In this model , semantic labels of page structure can be leveraged to help text content understanding , and semantic labels ofthe text phrases can be used in page structure understanding tasks such as data record detection . Thus , integration of both page structure and text content understanding leads to an integrated solution of webpage understanding . Experimental results on research homepage extraction show the feasibility and promise of our approach .
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