Relevance-based language models operate by estimating the probabilities of observing words in documents relevant (or pseudo
relevant) to a topic. However, these models assume that if a document is relevant to a topic, then all tokens in the documentare relevant to that topic. This could limit model robustness and effectiveness. In this study, we propose a Latent Dirichletrelevance model, which relaxes this assumption. Our approach derives from current research on Latent Dirichlet Allocation(LDA) topic models. LDA has been extensively explored, especially for discovering a set of topics from a corpus. LDA itself,however, has a limitation that is also addressed in our work. Topics generated by LDA from a corpus are synthetic, i.e., theydo not necessarily correspond to topics identified by humans for the same corpus. In contrast, our model explicitly considersthe relevance relationships between documents and given topics (queries). Thus unlike standard LDA, our model is directlyapplicable to goals such as relevance feedback for query modification and text classification, where topics (classes and queries)are provided upfront. Thus although the focus of our paper is on improving relevance-based language models, in effect ourapproach bridges relevance-based language models and LDA addressing limitations of both.
