document: Hierarchical topic segmentation of websites In this paper , we consider the problem of identifying and segmenting topically cohesive regions in the URL tree of a large website . Each page of the website is assumed to have a topic label or a distribution on topic labels generated using a standard classifier . We develop a set of cost measures characterizing the benefit accrued by introducing a segmentation of the site based on the topic labels . We propose a general framework to use these measures for describing the quality of a segmentation ; we also provide an efficient algorithm to find the best segmentation in this framework . Extensive experiments on human-labeled data confirm the soundness of our framework and suggest that a judicious choice of cost measures allows the algorithm to perform surprisingly accurate topical segmentations .
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