Topic segmentation model based on ATNLDA and co-occurrence theory and its application in stem cell field
作者: QingQiang WuCaiDong ZhangXinYing An
作者单位: 1School of Software, Xiamen University, P.R. China
2Institute of Medical Information, Chinese Academy of Medical Sciences, P.R. China
刊名: Journal of Information Science, 2013, Vol.39 (3), pp.319-332
来源数据库: Sage Publications, Inc.
DOI: 10.1177/0165551512457893
关键词: automatic topic number LDA (ATNLDA)correlation analysisco-occurrence theorylatent Dirichlet allocation (LDA)
原始语种摘要: This paper describes the application of co-occurrence and latent Dirichlet allocation (LDA)-based topic analyses in stem cell-related literature research. On account of the deficiency of parameter estimation in LDA, this study integrated co-occurrence theory and clustering judgement indicators and constructed an ATNLDA (Auto Topic Number LDA) model for topic segmentation. Next, ATNLDA was used to determine the optimal topic number of stem cell research literatures from 2006 to 2011 in PubMed, which was then used for topic segmentation of research content in stem cell data set. After stem cell research topics were obtained, they were analysed in terms of topic label, topic research content and interrelation between topics. The results verified that application of ATNLDA in topic...
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  • segmentation 分段
  • occurrence 事件
  • theory 理论
  • allocation 分配
  • latent 潜在的
  • interrelation 相互关系
  • research 
  • judgement 评价
  • model 模型
  • application 申请