A Polya Urn Document Language Model for Improved Information Retrieval

Abstract: The multinomial LLM 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 LLM that captures word burstiness directly. We show that the new reinforced LLM 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 LLM 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 LLM. 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 LLM 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.