A Survey on Advanced Algorithms in Topic Modeling
DOI:
https://doi.org/10.26438/ijcse/v6i5.428436Keywords:
Topic modeling, pLSI, LDA, Dynamical Topic Model, Supervised LDAAbstract
In this paper, Survey of various topic modeling algorithms is presented. Introduced classification differs from earlier efforts, providing a complementary view of the field. This survey provides a brief overview of the existing probabilistic topic models and gives motivation for future research.
References
A. Daud, J. Li, L. Zhou, and F. Muhammad, “Knowledge discovery through directed probabilistic topic models: a survey,” Frontiers of Computer Science in China, vol. 4, no. 2, pp. 280–301, Jun. 2010.
David M. Blei. Introduction to Probabilistic Topic Models. Communications of the ACM, 2011
Steyvers, M. and Griffiths, T., Probabilistic Topic Models. In T. Landauer, D. S. McNamara, S. Dennis, & W. Kintsch (Eds.), handbook of Latent Semantic Analysis. Hillsdale, NJ: Erlbaum, 2007
Jelisavcic, V., Furlan, B., Protic, J., & Milutinovic, V. M., “Topic Models and Advanced Algorithms for Profiling of Knowledge in Scientific Papers”, 35th International Convention on Information and Communication Technology, Electronics and Microelectronics (MIPRO’2012), 1030–1035.
Hofmann, T., Probabilistic Latent Semantic Indexing. In Proceedings of the 22nd ACM SIGIR Conference on Research & Development on Information Retrieval, Berkeley, CA, USA, 1999.
S. Deerwester, S. T. Dumais, G. W. Furnas, T. K. Landauer, and R. Harshman, “Indexing by latent semantic analysis”, Journal of the American Society for Information Science, vol. 41, pp. 391–407, 1990.
D. M. Blei, A. Y. Ng, and M. I. Jordan, “Latent dirichlet allocation,” Journal of Machine Learning Research, vol. 3, pp. 993–1022, Jan. 2003.
T. L. Griffiths and M. Steyvers, “Finding scientific topics”, Proceedings of the National Academy of Sciences of the United States of America, vol. 101, no. Suppl 1, pp. 5228–5235, Apr. 2004.
D. Blei, T. Gri, M. Jordan, and J. Tenenbaum, “Hierarchical topic models and the nested chinese restaurant process”, 2003.
D. M. Blei and J. D. Lafferty, “Dynamic topic models”, in Proceedings of the 23rd international conference on Machine learning, ser. ICML ’06. New York, NY, USA: ACM, 2006, pp. 113–120.
W. Li and A. McCallum, “Pachinko allocation: DAG-structured mixture models of topic correlations”, in Proceedings of the 23rd international conference on Machine learning, ser. ICML ’06. New York, NY, USA: ACM, 2006, pp. 577–58.
X. Wang and A. McCallum, “Topics over time: a non-Markov continuous-time model of topical trends”, in Proceedings of the 12th ACM SIGKDD international conference on Knowledge discovery and data mining, ser. KDD ’06. New York, NY, USA: ACM, 2006, pp. 424–433.
D. M. Blei and J. D. Lafferty, “Dynamic topic models”, in Proceedings of the 23rd international conference on Machine learning, ser. ICML ’06. New York, NY, USA: ACM, 2006, pp. 113–120.
M. R. Zvi, C. Chemudugunta, T. Griffiths, P. Smyth, and M. Steyvers, “Learning author-topic models from text corpora”, ACM Trans. Inf. Syst., vol. 28, no. 1, pp. 1–38, Jan. 2010.
D. Mimno and A. McCallum, “Topic Models Conditioned on Arbitrary Features with Dirichlet-multinomial Regression”, in Proceedings of the 24th Conference on Uncertainty in Artificial Intelligence (UAI ’08), 2008.
J.P. Yamron, I. Carp, L. Gillick, S. Lowe, and P. van Mulbregt, “A Hidden Markov Model Approach to Text Segmentation and Event Tracking”, Proceedings ICASSP-98, Seattle, May 1998.
D. M. Blei and J. D. Mcauliffe, “Supervised topic models”, in Proceedings of th Neural Information Processing Systems – NIPS, 2007.
Steyvers, Mark, Padhraic Smyth, and Chaitanya Chemuduganta. "Combining background knowledge and learned topics", Topics in Cognitive Science 3, no. 1 (2011): 18-47.
Zhu, J., Xing, E.P., “Conditional topic random fields”, Proc. 27th Int. Conf. Mach. Learn. 2010, 1239–1246.
Wang, Xuerui, Andrew McCallum, and Xing Wei. "Topical n-grams: Phrase and topic discovery, with an application to information retrieval" In Data Mining, 2007. ICDM 2007. Seventh IEEE International Conference on, pp. 697-702. IEEE, 2007.
Blei, David M., and Pedro J. Moreno. "Topic segmentation with an aspect hidden Markov model" In Proceedings of the 24th annual international ACM SIGIR conference on Research and development in information retrieval, pp. 343-348. ACM, 2001.
Bisgin, Halil, Zhichao Liu, Hong Fang, Xiaowei Xu, and Weida Tong. "Mining FDA drug labels using an unsupervised learning technique-topic modeling" BMC bioinformatics 12, no. 10 (2011): S11.
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