Bayesian extension to the language model for ad hoc information retrieval

We propose a Bayesian extension to the ad-hoc Language Model. Many smoothed estimators used for the multinomial query model in ad-hoc Language Models (including Laplace and Bayes-smoothing) are approximations to the Bayesian predictive distribution. In this paper we derive the full predictive distribution in a form amenable to implementation by classical IR models, and then compare it to other currently used estimators. In our experiments the proposed model outperforms Bayes-smoothing, and its combination with linear interpolation smoothing outperforms all other estimators. Categories and Subject Descriptors H [3]: 3—Retrieval models General Terms Algorithms, Performance, Theory Keywords Information Retrieval, Ad Hoc Retrieval, Ad Hoc Language Model, Bayesian Language Model

• Type: Conference
• Year: 2003
• Where: SIGIR
• Authors: Hugo Zaragoza, Djoerd Hiemstra, Michael E. Tipping
• Keywords: Ad-hoc Language Models, Language Model, Predictive Distribution, SIGIR 2003