We present a hybrid algorithm for Bayesian topic models that combines the efficiency of sparse Gibbs sampling with the scalability of online stochastic inference. We used our algorithm to analyze a corpus of 1.2 million books (33 billion words) with thousands of topics. Our approach reduces the bias of variational inference and generalizes to many Bayesian hidden-variable models.
Logistic-normal topic models can effectively discover correlation structures among latent topics. However, their inference remains a challenge because of the non-conjugacy between the logistic-normal prior and multinomial topic mixing proportions. Existing algorithms either make restricting mean-field assumptions or are not scalable to large-scale applications. This paper presents a partially collapsed Gibbs sampling algorithm that approaches the provably correct distribution by exploring the ideas of data augmentation. To improve time efficiency, we further present a parallel implementation that can deal with large-scale applications and learn the correlation structures of thousands of topics from millions of documents. Extensive empirical results demonstrate the promise.
Incorporating the temporal property of words into query expansion methods based on relevance feedback has been shown to have a significant positive effect on microblog search.In contrast to such word-based query expansion methods, we propose a concept-based query expansion method based on a temporal relevance model that uses the temporal variation of concepts (e.g., terms and phrases) on microblogs. Our model naturally extends an extremely effective existing concept-based relevance model by tracking the concept frequency over time.Moreover, the proposed model produces important concepts that are frequently used within a particular time periodassociated with a given topic, which better discriminate between relevant and non-relevant microblog documents than words.Our experiments using a corpus of microblog data (Tweets2011 corpus) show that the proposed concept-based query expansion method improves search performance significantly, especially for highly relevant documents.
Information Retrieval (IR) is concerned with the identification of documents in a collection that are relevant to a given information need, usually represented as a query containing terms or keywords, which are supposed to be a good description of what the user is looking for. IR systems may improve their effectiveness (i.e., increasing the number of relevant documents retrieved) by using a process of query expansion, which automatically adds new terms to the original query posed by an user. In this paper we develop a method of query expansion based on Bayesian networks. Using a learning algorithm, we construct a Bayesian network that represents some of the relationships among the terms appearing in a given document collection; this network is then used as a thesaurus (specific for that collection). We also report the results obtained by our method on three standard test collections.