publication . Preprint . 2016

Topic Browsing for Research Papers with Hierarchical Latent Tree Analysis

Poon, Leonard K. M.; Zhang, Nevin L.;
Open Access English
  • Published: 28 Sep 2016
Abstract
Academic researchers often need to face with a large collection of research papers in the literature. This problem may be even worse for postgraduate students who are new to a field and may not know where to start. To address this problem, we have developed an online catalog of research papers where the papers have been automatically categorized by a topic model. The catalog contains 7719 papers from the proceedings of two artificial intelligence conferences from 2000 to 2015. Rather than the commonly used Latent Dirichlet Allocation, we use a recently proposed method called hierarchical latent tree analysis for topic modeling. The resulting topic model contains...
Subjects
free text keywords: Computer Science - Computation and Language, Computer Science - Information Retrieval, Computer Science - Learning
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25 references, page 1 of 2

[2010] Blei, D. M.; Griffiths, T. L.; and Jordan, M. I. 2010.

The nested Chinese restaurant process and Bayesian nonparametric inference of topic hierarchies. Journal of the ACM 57(2):7:1-7:30.

[2003] Blei, D. M.; Ng, A. Y.; and Jordan, M. I. 2003. Latent Dirichlet allocation. Journal of Machine Learning Research 3:993-1022.

[2012] Blei, D. M. 2012. Probabilistic topic models. Communications of the ACM 55(4):77-84.

[2012] Chaney, A. J.-B., and Blei, D. M. 2012. Visualizing topic models. In International AAAI Conference on Web and Social Media.

[2012] Chen, T.; Zhang, N. L.; Liu, T.; Poon, K. M.; and Wang, Y. 2012. Model-based multidimensional clustering of categorical data. Artificial Intelligence 176:2246-2269.

[2016] Chen, P.; Zhang, N. L.; Poon, L. K. M.; and Chen, Z. 2016. Progressive EM for latent tree models and hierarchical topic detection. In Proceedings of the Thirtieth AAAI Conference on Artificial Intelligence.

[1968] Chow, C. K., and Liu, C. N. 1968. Approximating discrete probability distributions with dependence trees. [OpenAIRE]

IEEE Transactions on Information Theory 14(3):462-467.

[2006] Cover, T. M., and Thomas, J. A. 2006. Elements of Information Theory. Wiley, 2nd edition.

[2016] Deng, K.; Bol, P. K.; Li, K. J.; and Liu, J. S. 2016. On the unsupervised analysis of domain-specific chinese texts. [OpenAIRE]

Proceedings of the National Academy of Sciences of the United States of America 113(22):6154-6159.

[2010] Gardner, M. J.; Lutes, J.; Lund, J.; Hansen, J.; Walker, D.; Ringger, E.; and Seppi, K. 2010. The topic browser: An interactive tool for browsing topic models. In NIPS Workshop on Challenges of Data Visualization.

[2015] Liu, T.-F.; Zhang, N. L.; Chen, P.; Liu, A. H.; Poon, L. K.; and Wang, Y. 2015. Greedy learning of latent tree models for multidimensional clustering. Machine Learning 98(1-2):301-330.

[2014] Liu, T.; Zhang, N. L.; and Chen, P. 2014. Hierarchical latent tree analysis for topic detection. In Machine Learning and Knowledge Discovery in Databases (ECML PKDD 2014), volume 8725 of Lecture Notes in Computer Science.

25 references, page 1 of 2
Related research
Abstract
Academic researchers often need to face with a large collection of research papers in the literature. This problem may be even worse for postgraduate students who are new to a field and may not know where to start. To address this problem, we have developed an online catalog of research papers where the papers have been automatically categorized by a topic model. The catalog contains 7719 papers from the proceedings of two artificial intelligence conferences from 2000 to 2015. Rather than the commonly used Latent Dirichlet Allocation, we use a recently proposed method called hierarchical latent tree analysis for topic modeling. The resulting topic model contains...
Subjects
free text keywords: Computer Science - Computation and Language, Computer Science - Information Retrieval, Computer Science - Learning
Download from
25 references, page 1 of 2

[2010] Blei, D. M.; Griffiths, T. L.; and Jordan, M. I. 2010.

The nested Chinese restaurant process and Bayesian nonparametric inference of topic hierarchies. Journal of the ACM 57(2):7:1-7:30.

[2003] Blei, D. M.; Ng, A. Y.; and Jordan, M. I. 2003. Latent Dirichlet allocation. Journal of Machine Learning Research 3:993-1022.

[2012] Blei, D. M. 2012. Probabilistic topic models. Communications of the ACM 55(4):77-84.

[2012] Chaney, A. J.-B., and Blei, D. M. 2012. Visualizing topic models. In International AAAI Conference on Web and Social Media.

[2012] Chen, T.; Zhang, N. L.; Liu, T.; Poon, K. M.; and Wang, Y. 2012. Model-based multidimensional clustering of categorical data. Artificial Intelligence 176:2246-2269.

[2016] Chen, P.; Zhang, N. L.; Poon, L. K. M.; and Chen, Z. 2016. Progressive EM for latent tree models and hierarchical topic detection. In Proceedings of the Thirtieth AAAI Conference on Artificial Intelligence.

[1968] Chow, C. K., and Liu, C. N. 1968. Approximating discrete probability distributions with dependence trees. [OpenAIRE]

IEEE Transactions on Information Theory 14(3):462-467.

[2006] Cover, T. M., and Thomas, J. A. 2006. Elements of Information Theory. Wiley, 2nd edition.

[2016] Deng, K.; Bol, P. K.; Li, K. J.; and Liu, J. S. 2016. On the unsupervised analysis of domain-specific chinese texts. [OpenAIRE]

Proceedings of the National Academy of Sciences of the United States of America 113(22):6154-6159.

[2010] Gardner, M. J.; Lutes, J.; Lund, J.; Hansen, J.; Walker, D.; Ringger, E.; and Seppi, K. 2010. The topic browser: An interactive tool for browsing topic models. In NIPS Workshop on Challenges of Data Visualization.

[2015] Liu, T.-F.; Zhang, N. L.; Chen, P.; Liu, A. H.; Poon, L. K.; and Wang, Y. 2015. Greedy learning of latent tree models for multidimensional clustering. Machine Learning 98(1-2):301-330.

[2014] Liu, T.; Zhang, N. L.; and Chen, P. 2014. Hierarchical latent tree analysis for topic detection. In Machine Learning and Knowledge Discovery in Databases (ECML PKDD 2014), volume 8725 of Lecture Notes in Computer Science.

25 references, page 1 of 2
Related research
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