Improving pseudo-relevance feedback in web information retrieval using web page segmentation

  • Authors:

  • Shipeng Yu;Deng Cai;Ji-Rong Wen;Wei-Ying Ma

  • Affiliations:

  • Peking University, Beijing, China;Tsinghua University, Beijing, China;Microsoft Research Asia, Beijing, China;Microsoft Research Asia, Beijing, China

  • Venue:
  • WWW '03 Proceedings of the 12th international conference on World Wide Web
  • Year:
  • 2003

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Abstract

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.