Using Relevance Feedback within the Relational Model for TREC-5

For TREC-5, we enhanced our existing prototype that implements relevance ranking using the AT&T DBC-1012 Model 4 parallel database machine to include relevance feedback. We identified SQL to compute relevance feedback and ran several experiments to identify good cutoffs for the number of documents that should be assumed to be relevant and the number of terms to add to a query. We also tried to find an optimal weighting scheme such that terms added by relevance feedback are weighted differently from those in the original query. We implemented relevance feedback in our special purpose IR prototype. Additionally, we used relevance feedback as a part of our submissions for English, Spanish, Chinese and corrupted data. Finally, we were a participant in the large data track as well. We used a text merging approach whereby a single Pentium processor was able to implement adhoc retrieval on a 4GB text collection. * This work supported in part by the National Science Foundation under contract number IRI9357785 and industrial matching funds under the National Young Investigator Program. Ophir Frieder is currently on leave from the Department of Computer Science at George Mason University.