基于LDA话题关联的话题演化

[1] On-Line LDA: Adaptive Topic Models for Mining Text Streams with Applications to Topic Detection and Tracking. L AlSumait,D Barbar‘a,C Domeniconi. IEEE International Conference on Data Mining . 2008

[2] Latent Dirichlet allocation. Blei D M,Ng A Y,Jordan M I. Journal of Machine Learning Research . 2003

[3] Dynamic topic models. Blei D M,Lafferty J D. Proceedings of the 23rd International Conference onMachine Learning . 2006

[4] Topic over time:A non-mark-ov continuous-time model of topical trends. Wang X,,McCallum A. ACMSIGKDD-2006 . 2006

[5] Studying the History of Ideas Using Topic Models. David Hall,Daniel Jurafsky,Christopher D Manning. Proceedings of the 2008 Conference on Empirical Methods in Natural Language Processing . 2008

[6] Finding scientific topics. Griffiths T L,Steyvers M. Proc.National Academy of Science . 2004

[1] On-Line LDA: Adaptive Topic Models for Mining Text Streams with Applications to Topic Detection and Tracking. L AlSumait,D Barbar‘a,C Domeniconi. IEEE International Conference on Data Mining . 2008

[2] Latent Dirichlet allocation. Blei D M,Ng A Y,Jordan M I. Journal of Machine Learning Research . 2003

[3] Dynamic topic models. Blei D M,Lafferty J D. Proceedings of the 23rd International Conference onMachine Learning . 2006

[4] Topic over time:A non-mark-ov continuous-time model of topical trends. Wang X,,McCallum A. ACMSIGKDD-2006 . 2006

[5] Studying the History of Ideas Using Topic Models. David Hall,Daniel Jurafsky,Christopher D Manning. Proceedings of the 2008 Conference on Empirical Methods in Natural Language Processing . 2008

[6] Finding scientific topics. Griffiths T L,Steyvers M. Proc.National Academy of Science . 2004