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.
|Title of host publication||NIST Special Publication|
|Publication status||Published - 2009|
|Event||18th Text REtrieval Conference, TREC 2009 - Gaithersburg, MD, United States|
Duration: 17 Nov 2009 → 20 Nov 2009
|Other||18th Text REtrieval Conference, TREC 2009|
|Period||17/11/09 → 20/11/09|
ASJC Scopus subject areas