Relevance-Based Language Models : Estimation and Analysis

It has long been recognized that the primary obstacle to effective performance of classical models is the need to estimate a relevance model with no training data. We propose a novel technique for estimating such models using the query alone. We demonstrate that our technique can produce highly accurate relevance models. Our experiments show relevance models outperforming baseline language modeling systems on TREC retrieval. The main contribution of this work is an effective formal method for estimating a relevance model with no training data.