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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
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.
Wang Ping . Topic Extraction and Evolution for Scientific Literature Based on Hierarchical Probabilistic Topic Model[J]. Library and Information Service, 2014 , 58(22) : 70 -77 . DOI: 10.13266/j.issn.0252-3116.2014.22.012
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