A Polya Urn Document Language Model for Improved Information Retrieval

TL;DR

This paper introduces a Polya urn-based language model that captures term burstiness, improving information retrieval effectiveness and robustness over traditional multinomial models, with theoretical and empirical validation.

Contribution

It develops a Dirichlet compound multinomial language model using a Polya process to model word burstiness, enhancing retrieval performance and robustness.

Findings

Significantly outperforms state-of-the-art language models on TREC collections.

Model tuning is more robust compared to multinomial models.

The model aligns with the verbosity hypothesis and relates to tf-idf schemes.

Abstract

The multinomial language model has been one of the most effective models of retrieval for over a decade. However, the multinomial distribution does not model one important linguistic phenomenon relating to term-dependency, that is the tendency of a term to repeat itself within a document (i.e. word burstiness). In this article, we model document generation as a random process with reinforcement (a multivariate Polya process) and develop a Dirichlet compound multinomial language model that captures word burstiness directly. We show that the new reinforced language model can be computed as efficiently as current retrieval models, and with experiments on an extensive set of TREC collections, we show that it significantly outperforms the state-of-the-art language model for a number of standard effectiveness metrics. Experiments also show that the tuning parameter in the proposed model is more robust than in the multinomial language model. Furthermore, we develop a constraint for the verbosity hypothesis and show that the proposed model adheres to the constraint. Finally, we show that the new language model essentially introduces a measure closely related to idf which gives theoretical justification for combining the term and document event spaces in tf-idf type schemes.