Passage Based Answer-Set Graph Approach for Query Performance Prediction

Approaches involving the use of post-retrieval information for a given query have been adopted in a variety of ways in the past for query performance prediction (QPP) tasks. Researchers have utilized information via document retrieval as well as passage retrieval approaches for QPP. We present a novel approach of representing the top returned passages (answer-set) as a graph where each node represents a passage and an edge weight indicates the similarity score between these passages. By examining the answer-set graph we developed new predictors that utilizes graph features such as cohesion and minimum spanning tree. Based on the empirical evaluation, we show that our answer-set graph predictors are very effective and perform even better (for Cranfield and Ohsumed Collection) than the current state-of-the-art QPP approaches.