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Topic Evolution in Scientific Literature |
He Liang, Li Fang |
Department of Computer Science & Engineering, Shanghai Jiaotong University, Shanghai 200240, China |
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Abstract This paper uses LDA model to generate topics from the scientific literature,then calculates the strength and feature key to find the evolution trends of topics. The experiments on NIPS anthology and ACL anthology show the trends of machine learning and computational linguistics, and also prove the feasibility of the proposed calculating method.
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Received: 30 January 2012
Published: 20 May 2012
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