Active Feedback - UIUC TREC-2003 HARD Experiments

In this paper, we report our experiments on the HARD (High Accuracy Retrieval from Documents) Track in TREC 2003. We focus on active feedback, i.e., how to intelligently propose questions for relevance feedback in order to maximize accuracy improvement in the second run. We proposed and empirically evaluated three different methods, i.e., top-k, gapped top-k, and k-cluster centroid, to extract a fixed number of text units (e.g. passage or document) for feedback. The results show that presenting the top k documents for user feedback is often not as beneficial for learning as presenting more diversified documents.

• Type: Conference
• Year: 2003
• Where: TREC
• Authors: Xuehua Shen, ChengXiang Zhai
• Keywords: Accuracy Improvement, Active Feedback, Relevance Feedback, TREC 2003, TREC 2008