[Objective] To detect the birth, extinction, development, merge and split of topic evolution of the literatures in a certain field. [Methods] This paper divides time windows according to the publication data of the literatures, and LDA model is applied to extract topics from each time window automatically. The topic association filter rules are used to determine evolution relationships between topics in adjacent time windows. Form a topic evolution path in a continuous time period. [Results] Considering the continuity of the topics, different types of topic evolution could be detected with high accuracy. [Limitations] This method fixes the size of time windows without considering the diversity of topic evolution cycles. [Conclusions] This method can effectively reduce the interference of topics with smaller similarity in LDA, and enhance accuracy of evolution relation recognition.
秦晓慧, 乐小虬. 基于LDA主题关联过滤的领域主题演化研究[J]. 现代图书情报技术, 2015, 31(3): 18-25.
Qin Xiaohui, Le Xiaoqiu. Topic Evolution Research on a Certain Field Based on LDA Topic Association Filter. New Technology of Library and Information Service, DOI：10.11925/infotech.1003-3513.2015.03.03.
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