Abstract
Rank-integrated topic models which incorporate link structures into topic modeling through topical ranking have shown promising performance comparing to other link combined topic models. However, existing work on rank-integrated topic modeling treats ranking as document distribution for topic, and therefore can’t integrate topical ranking with LDA model, which is one of the most popular topic models. In this paper, we introduce a new method to integrate topical ranking with topic modeling and propose a general framework for topic modeling of documents with link structures. By interpreting the normalized topical ranking score vectors as topic distributions for documents, we fuse ranking into topic modeling in a general framework. Under this general framework, we construct two rank-integrated PLSA models and two rank-integrated LDA models, and present the corresponding learning algorithms. We apply our models on four real datasets and compare them with baseline topic models and the state-of-the-art link combined topic models in generalization performance, document classification, document clustering and topic interpretability. Experiments show that all rank-integrated topic models perform better than baseline models, and rank-integrated LDA models outperform all the compared models.
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Acknowledgments
This work is supported by the National Key Research and Development Program of China under grants 2016QY01W0202 and 2016YFB08-00402, National Natural Science Foundation of China under grants 61572221, U1401258, 61433006 and 61502185, Major Projects of the National Social Science Foundation under grant 16ZDA092, Science and Technology Support Program of Hubei Province under grant 2015AAA013, Science and Technology Program of Guangdong Province under grant 2014B010111007, and Guangxi High Level Innovation Team in Higher Education Institutions - Innovation Team of ASEAN Digital Cloud Big Data Security and Mining Technology.
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Zhang, Z., Li, R., Li, Y., Gu, X. (2018). Rank-Integrated Topic Modeling: A General Framework. In: Cai, Y., Ishikawa, Y., Xu, J. (eds) Web and Big Data. APWeb-WAIM 2018. Lecture Notes in Computer Science(), vol 10987. Springer, Cham. https://doi.org/10.1007/978-3-319-96890-2_2
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DOI: https://doi.org/10.1007/978-3-319-96890-2_2
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