Ranking Document Clusters Using Markov Random Fields

An important challenge in cluster-based document retrieval is ranking document clusters by their relevance to the query. We present a novel cluster ranking approach that utilizes Markov Random Fields (MRFs). MRFs enable the integration of various types of cluster-relevance evidence; e.g., the query-similarity values of the cluster's documents and query-independent measures of the cluster. We use our method to re-rank an initially retrieved document list by ranking clusters that are created from the documents most highly ranked in the list. The resultant retrieval effectiveness is substantially better than that of the initial list for several lists that are produced by effective retrieval methods. Furthermore, our cluster ranking approach significantly outperforms state-of-the-art cluster ranking methods. We also show that our method can be used to improve the performance of (state-of-the-art) results-diversification methods.