Query Expansion for the Language Modelling Framework Using the Naïve Bayes Assumption

Abstract

Language modelling is new form of information retrieval that is rapidly becoming the preferred choice over probabilistic and vector space models, due to the intuitiveness of the model formulation and its effectiveness. The language model assumes that all terms are independent, therefore the majority of the documents returned to the ser will be those that contain the query terms. By making this assumption, related documents that do not contain the query terms will never be found, unless the related terms are introduced into the query using a query expansion technique. Unfortunately, recent attempts at performing a query expansion using a language model have not been in-line with the language model, being complex and not intuitive to the user. In this article, we introduce a simple method of query expansion using the naïve Bayes assumption, that is in-line with the language model since it is derived from the language model. We show how to derive the query expansion term relationships using probabilistic latent semantic analysis (PLSA). Through experimentation, we show that using PLSA query expansion within the language model framework, we can provide a significant increase in precision.