Paper: Structural Topic Model for Latent Topical Structure Analysis

ACL ID P11-1153
Title Structural Topic Model for Latent Topical Structure Analysis
Venue Annual Meeting of the Association of Computational Linguistics
Session Main Conference
Year 2011

Topic models have been successfully applied to many document analysis tasks to discover topics embedded in text. However, existing topic models generally cannot capture the la- tent topical structures in documents. Since languagesareintrinsicallycohesiveandcoher- ent, modeling and discovering latent topical transition structures within documents would be beneficial for many text analysis tasks. In this work, we propose a new topic model, StructuralTopicModel, whichsimultaneously discovers topics and reveals the latent topi- cal structures in text through explicitly model- ing topical transitions with a latent first-order Markov chain. Experiment results show that the proposed Structural Topic Model can ef- fectively discover topical structures in text, and the identified structures signifi...