{"id":152655,"date":"2002-12-01T00:00:00","date_gmt":"2002-12-01T00:00:00","guid":{"rendered":"https:\/\/cm-edgetun.pages.dev\/en-us\/research\/msr-research-item\/improving-pseudo-relevance-feedback-in-web-information-retrieval-using-web-page-segmentation\/"},"modified":"2018-10-16T20:08:31","modified_gmt":"2018-10-17T03:08:31","slug":"improving-pseudo-relevance-feedback-in-web-information-retrieval-using-web-page-segmentation","status":"publish","type":"msr-research-item","link":"https:\/\/cm-edgetun.pages.dev\/en-us\/research\/publication\/improving-pseudo-relevance-feedback-in-web-information-retrieval-using-web-page-segmentation\/","title":{"rendered":"Improving Pseudo-Relevance Feedback in Web Information Retrieval Using Web Page Segmentation"},"content":{"rendered":"<div class=\"asset-content\">\n<p>In contrast to traditional document retrieval, a web page as a whole is not a good information unit to search because it often contains multiple topics and a lot of irrelevant information from navigation, decoration, and interaction part of the page. In this paper, we propose a VIsion-based Page Segmentation (VIPS) algorithm to detect the semantic content structure in a web page. Compared with simple DOM based segmentation method, our page segmentation scheme utilizes useful visual cues to obtain a better partition of a page at the semantic level. By using our VIPS algorithm to assist the selection of query expansion terms in pseudo-relevance feedback in web information retrieval, we achieve 27% performance improvement on Web Track dataset.<\/p>\n<\/div>\n<p><!-- .asset-content --><\/p>\n","protected":false},"excerpt":{"rendered":"<p>In contrast to traditional document retrieval, a web page as a whole is not a good information unit to search because it often contains multiple topics and a lot of irrelevant information from navigation, decoration, and interaction part of the page. 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