A Latent Dirichlet Framework for Relevance Modeling

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 document are relevant to that topic. This could limit model robustness and effectiveness. In this study, we propose a Latent Dirichlet relevance 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 generating a set of topics from a corpus. A key attraction is that in LDA a document may be about several topics. LDA itself, however, has a limitation that is also addressed in our work. Topics generated by LDA from a corpus are synthetic, i.e., they do not necessarily correspond to topics identified by humans for the same corpus. In contrast, our model explicitly considers the relevance relationships between documents and give...

• Type: Conference
• Year: 2009
• Where: AIRS
• Authors: Viet Ha-Thuc, Padmini Srinivasan
• Keywords: AIRS 2009, Documents, Information Retrieval, Latent Dirichlet, Relevance-based Language Models