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ETM: Entity Topic Models for Mining Documents Associated with Entities Hyungsul Kim, Yizhou Sun, Julia Hockenmaier and Jiawei Han University of Illinois at Urbana-Champaign {hkim21, sun22, juliahmr, hanj}@ Abstract—Topic models, which factor each document into different topics and represent each topic as a distribution of terms, have been widely and successfully used to better understand collections of text documents. However, documents are also associated with further information, such as the set of real-world entities mentioned in them. For example, news articles are usually related to several people, organizations, countries or locations. Since those associated entities carry rich information, it is highly desirable to build more expressive, entity-based topic models, which can capture the term distributions for each topic, each entity, as well as each topic-entity pair. In this paper, we therefore introduce a novel Entity Topic Model (ETM) for documents that are associated with a set of entities. ETM not only models the generative process of a term given its topic and entity information, but also models the correlation of entity term distributions and topic term distributions. A Gibbs sampling-based algorithm is proposed to learn the model. Experiments on real datasets demonstrate the effectiveness of our approach over several state-of-the-art baselines. Keywords-topic models; data mining; entity; I. INTRODUCTION Starting with the great success of Probabilistic Latent Semantic Analysis (PLSA) [8] and Latent Dirichlet Allocation (LDA) [4], there have been numerous proposals for topic models that identify patterns of word occurrences in large collections of documents which re?ect the underlying topics represented in the collection, and can then be used to organize, search, index and browse large collection of documents [21]. While traditional topic models treat each document as a bag of words, documents are in fact associated with richer attributes: for example, n