Paper: Modelling Sequential Text with an Adaptive Topic Model

ACL ID D12-1049
Title Modelling Sequential Text with an Adaptive Topic Model
Venue Conference on Empirical Methods in Natural Language Processing
Session Main Conference
Year 2012
Authors

Topic models are increasingly being used for text analysis tasks, often times replacing ear- lier semantic techniques such as latent seman- tic analysis. In this paper, we develop a novel adaptive topic model with the ability to adapt topics from both the previous segment and the parent document. For this proposed model, a Gibbs sampler is developed for doing poste- rior inference. Experimental results show that with topic adaptation, our model significantly improves over existing approaches in terms of perplexity, and is able to uncover clear se- quential structure on, for example, Herman Melville?s book ?Moby Dick?.