收稿日期: 2014-09-01
修回日期: 2014-11-05
网络出版日期: 2014-11-20
基金资助
本文系国家自然科学基金青年科学基金项目“多因素融合下的微博话题可信度评估模型及实证研究”(项目编号:71303179)研究成果之一.
Topic Extraction and Evolution for Scientific Literature Based on Hierarchical Probabilistic Topic Model
Received date: 2014-09-01
Revised date: 2014-11-05
Online published: 2014-11-20
王平 . 基于层次概率主题模型的科技文献主题发现及演化[J]. 图书情报工作, 2014 , 58(22) : 70 -77 . DOI: 10.13266/j.issn.0252-3116.2014.22.012
Automatic mining scientific literature's topic and observing topic change for researchers will play great role in understanding and accessing the latest research frontiers on certain field. This paper analyzed topic extraction and evolution approaches of scientific papers by examining the characteristics of the diversity and dynamics of scientific papers, and based on hierarchical probabilistic topic model, using Gibbs sampling to estimate the model parameters and choosing the high-quality topic words by means of mutual information. This paper finally used Pro/Post-discretized analysis to study the topic evolution. The experimental results show that topic extraction and evolution method proposed in this paper are feasible and effective.
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