Abstract
New academic papers appear rapidly in the literature nowadays. This poses a challenge for researchers who are trying to keep up with a given field, especially those who are new to a field and may not know where to start from. To address this kind of problems, we have developed a topic browsing system for research papers where the papers have been automatically categorized by a probabilistic topic model. Rather than using Latent Dirichlet Allocation (LDA) for topic modeling, we use a recently proposed method called hierarchical latent tree analysis, which has been shown to perform better than some state-of-the-art LDA-based methods. The resulting topic model contains a hierarchy of topics so that users can browse topics at different levels. The topic model contains a manageable number of general topics at the top level and allows thousands of fine-grained topics at the bottom level.
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References
Blei, D.M., Griffiths, T.L., Jordan, M.I.: The nested Chinese restaurant process and Bayesian nonparametric inference of topic hierarchies. J. ACM 57(2), 7:1–7:30 (2010)
Blei, D.M., Ng, A.Y., Jordan, M.I.: Latent Dirichlet allocation. J. Mach. Learn. Res. 3, 993–1022 (2003)
Chen, P., Zhang, N.L., Poon, L.K.M., Chen, Z.: Progressive EM for latent tree models and hierarchical topic detection. In: Proceedings of the Thirtieth AAAI Conference on Artificial Intelligence (2016)
Chen, T., Zhang, N.L., Liu, T., Poon, K.M., Wang, Y.: Model-based multidimensional clustering of categorical data. Artif. Intell. 176, 2246–2269 (2012)
Paisley, J., Wang, C., Blei, D.M., Jordan, M.I.: Nested hierarchical Dirichlet processes. IEEE Trans. Pattern Anal. Mach. Intell. 37(2), 256–270 (2015)
Acknowledgment
The work was supported by the Education University of Hong Kong under project RG90/2014-2015R and Hong Kong Research Grants Council under grants 16202515 and 16212516.
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Poon, L.K.M., Leung, C.F., Chen, P., Zhang, N.L. (2017). Topic Browsing System for Research Papers Based on Hierarchical Latent Tree Analysis. In: Chen, L., Jensen, C., Shahabi, C., Yang, X., Lian, X. (eds) Web and Big Data. APWeb-WAIM 2017. Lecture Notes in Computer Science(), vol 10367. Springer, Cham. https://doi.org/10.1007/978-3-319-63564-4_32
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DOI: https://doi.org/10.1007/978-3-319-63564-4_32
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