Relevance based language models

TLDR

We investigate how classical probabilistic IR models relate to emerging language‑modeling approaches, noting that their main limitation is the difficulty of estimating relevance‑model word probabilities. The authors propose a query‑only technique to estimate these relevance‑model probabilities. The method produces highly accurate relevance models that outperform baseline language‑model systems on TREC retrieval and TDT tracking, offering a formal, training‑data‑free approach.

Abstract

We explore the relation between classical probabilistic models of information retrieval and the emerging language modeling approaches. It has long been recognized that the primary obstacle to effective performance of classical models is the need to estimate arelevance model: probabilities of words in the relevant class. We propose a novel technique for estimating these probabilities using the query alone. We demonstrate that our technique can produce highly accurate relevance models, addressing important notions of synonymy and polysemy. Our experiments show relevance models outperforming baseline language modeling systems on TREC retrieval and TDT tracking tasks. The main contribution of this work is an effective formal method for estimating a relevance model with no training data.