Using coherence-based measures to predict query difficulty

We investigate the potential of coherence-based scores to predict query difficulty. The coherence of a document set associated with each query word is used to capture the quality of a query topic aspect. A simple query coherence score, QC-1, is proposed that requires the average coherence contribution of individual query terms to be high. Two further query scores, QC-2 and QC-3, are developed by constraining QG-1 in order to capture the semantic similarity among query topic aspects. All three query coherence scores show the correlation with average precision necessary to make them good predictors of query difficulty. Simple and efficient, the measures require no training data and are competitive with language model-based clarity scores.