Modelling sequential text with an adaptive topic model

Authors:
Lan Du;Wray Buntine;Huidong Jin
Affiliations:
Macquarie University, Sydney, Australia;National ICT Australia, Canberra, Australia;CSIRO Mathematics, Informatics and Statistics, Canberra, Australia
Venue:
EMNLP-CoNLL '12 Proceedings of the 2012 Joint Conference on Empirical Methods in Natural Language Processing and Computational Natural Language Learning
Year:
2012

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Abstract

Topic models are increasingly being used for text analysis tasks, often times replacing earlier semantic techniques such as latent semantic 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 posterior 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 sequential structure on, for example, Herman Melville's book "Moby Dick".