Accession Number : ADA517808


Title :   Mining Specific and General Features in Both Positive and Negative Relevance Feedback. QUT E-Discovery Lab at the TREC'09 Relevance Feedback Track


Descriptive Note : Conference paper


Corporate Author : QUEENSLAND UNIV (AUSTRALIA)


Personal Author(s) : Li, Yuefeng ; Tao, Xiaohui ; Algarni, Abdulmohsen ; Wu, Sheng-Tang


Full Text : https://apps.dtic.mil/dtic/tr/fulltext/u2/a517808.pdf


Report Date : Nov 2009


Pagination or Media Count : 13


Abstract : User relevance feedback is usually utilized by Web systems to interpret user information needs and retrieve effective results for users. However, how to discover useful knowledge in user relevance feedback and how to wisely use the discovery knowledge are two critical problems. In TREC 2009, we participated in the Relevance Feedback Track and experimented a model consisting of two innovative stages: one for subject-based query expansion to extract pseudo-relevance feedback; one for relevance feature discovery to find useful patterns and terms in relevance judgements to rank documents. In this paper, the detailed description of our model is given, as well as the related discussions for the experimental results.


Descriptors :   *INTERNET BROWSERS , *FEEDBACK , *INFORMATION RETRIEVAL , TEXT PROCESSING , AUSTRALIA , ONTOLOGY , SYMPOSIA , USER NEEDS


Subject Categories : Information Science
      Computer Programming and Software
      Cybernetics


Distribution Statement : APPROVED FOR PUBLIC RELEASE