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Frontiers of Information Technology & Electronic Engineering >> 2017, Volume 18, Issue 10 doi: 10.1631/FITEE.1601125

Topic discovery and evolution in scientific literature based on content and citations

. College of Information Science & Electronic Engineering, Zhejiang University, Hangzhou 310027, China.. State Key Lab of CAD & CG, Zhejiang University, Hangzhou 310027, China.. School of Information Engineering, Zhejiang A&F University, Lin’an 311300, China.. Zhejiang Provincial Key Laboratory of Forestry Intelligent Monitoring and Information Technology, Lin’an 311300, China

Available online: 2018-01-18

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Abstract

Researchers across the globe have been increasingly interested in the manner in which important research topics evolve over time within the corpus of scientific literature. In a dataset of scientific articles, each document can be considered to comprise both the words of the document itself and its citations of other documents. In this paper, we propose a citation- content-latent Dirichlet allocation (LDA) topic discovery method that accounts for both document citation relations and the con-tent of the document itself via a probabilistic generative model. The citation-content-LDA topic model exploits a two-level topic model that includes the citation information for ‘father’ topics and text information for sub-topics. The model parameters are estimated by a collapsed Gibbs sampling algorithm. We also propose a topic evolution algorithm that runs in two steps: topic segmentation and topic dependency relation calculation. We have tested the proposed citation-content-LDA model and topic evolution algorithm on two online datasets, (PAMI) and IEEE Computer Society (CS), to demonstrate that our algorithm effectively discovers important topics and reflects the topic evolution of important research themes. According to our evaluation metrics, citation-content-LDA outperforms both content-LDA and citation-LDA.

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