Learning to rank query reformulations

Query reformulation techniques based on query logs have recently proven to be effective for web queries. However, when initial queries have reasonably good quality, these techniques are often not reliable enough to identify the helpful reformulations among the suggested queries. In this paper, we show that we can use as few as two features to rerank a list of reformulated queries, or expanded queries to be specific, generated by a log-based query reformulation technique. Our results across five TREC collections suggest that there are consistently more useful reformulations in the first positions in the new ranked list than there were initially, which leads to statistically significant improvements in retrieval effectiveness. Categories and Subject Descriptors H.3.3 [Information Search and Retrieval]: Query Formulation General Terms Algorithms, Measurement, Performance, Experimentation. Keywords Query reformulation, query expansion, query log, query performance predictor, learning to r...

• Type: Conference
• Year: 2010
• Where: SIGIR
• Authors: Van Dang, Michael Bendersky, W. Bruce Croft
• Keywords: Information Technology, Query Log, Query Reformulation, Query Reformulation Technique, SIGIR 2010