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 of the 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.