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.
贺亮, 李芳. 科技文献话题演化研究[J]. 现代图书情报技术, 2012, 28(4): 61-67.
He Liang, Li Fang. Topic Evolution in Scientific Literature. New Technology of Library and Information Service, 2012, 28(4): 61-67.
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