CMIC@TREC-2009: Relevance feedback track

Kareem Darwish, Ahmed El-Deeb

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

This paper describes CMIC's submissions to the TREC'09 relevance feedback track. In the phase 1 runs we submitted, we experimented with two different techniques to produce 5 documents to be judged by the user in the initial feedback step, namely using knowledge bases and clustering. Both techniques attempt to topically diversify these 5 documents as much as possible in an effort to maximize the probability that they contain at least 1 relevant document. The basic premise is that if a query has n diverse interpretations, then diversifying results and picking the top 5 most likely interpretations would maximize the probability that a user would be interested in at least one interpretation. In phase 2 runs, which involved the use of the feedback attained from phase 1 judgments, we attempted to use positive and negative judgments in weighing the terms to be used for subsequent feedback.

Original languageEnglish
Title of host publicationNIST Special Publication
Publication statusPublished - 2009
Externally publishedYes
Event18th Text REtrieval Conference, TREC 2009 - Gaithersburg, MD, United States
Duration: 17 Nov 200920 Nov 2009

Other

Other18th Text REtrieval Conference, TREC 2009
CountryUnited States
CityGaithersburg, MD
Period17/11/0920/11/09

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ASJC Scopus subject areas

  • Engineering(all)

Cite this

Darwish, K., & El-Deeb, A. (2009). CMIC@TREC-2009: Relevance feedback track. In NIST Special Publication