Improved Query Topic Models via Pseudo-Relevant P\'olya Document Models

TL;DR

This paper introduces a novel query expansion method using a Pólya-based language model to better identify topical terms from pseudo-relevant documents, improving retrieval effectiveness over existing methods.

Contribution

It develops a new language modeling framework assuming documents are generated by Pólya distributions, enabling more accurate query topic modeling for information retrieval.

Findings

Outperforms current state-of-the-art expansion methods on TREC collections

Effectively identifies topical terms using Pólya distribution assumptions

Enhances retrieval effectiveness through improved query modeling

Abstract

Query-expansion via pseudo-relevance feedback is a popular method of overcoming the problem of vocabulary mismatch and of increasing average retrieval effectiveness. In this paper, we develop a new method that estimates a query topic model from a set of pseudo-relevant documents using a new language modelling framework. We assume that documents are generated via a mixture of multivariate Polya distributions, and we show that by identifying the topical terms in each document, we can appropriately select terms that are likely to belong to the query topic model. The results of experiments on several TREC collections show that the new approach compares favourably to current state-of-the-art expansion methods.