CMIC@TREC-2009: Relevance Feedback Track
CAIRO MICROSOFT INNOVATION CENTER (CMIC) ABOU RAWASH (EGYPT)
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This paper describes CMICs submissions to the TREC09 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.
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