Journal
IEEE ACCESS
Volume 7, Issue -, Pages 6386-6399Publisher
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/ACCESS.2018.2887314
Keywords
Research topics mining; dynamics of research topics; latent Dirichlet allocation; WordNet; large corpora
Categories
Funding
- National Natural Science Foundation of China [61702306, 61472229, 61602278]
- Ministry of Education of the China Foundation for Humanities and Social Sciences [16YJCZH041, 17YJCZH262, ZR2018BF013]
- Shandong Provincial Natural Science Foundation of China [ZR2017BF015, 2016ZDJS02A11, ZR2017MF027]
- Taishan Scholar Climbing Program of Shandong Province
- SDUST Research Fund [2015TDJH102]
- Open Project of the Key Laboratory of Embedded System and Service Computing, Tongji University [ESSCKF 2016-06]
- Scientific Research Foundation of the Shandong University of Science and Technology for Recruited Talents [2016RCJJ011]
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A large volume of research documents are available online for us to access and analysis. It is very important to detect and mine the dynamics of the research topics from these large corpora. In this paper, we propose an improved method by introducing WordNet to LDA. This approach is to find latent topics of large corpora, and then we propose many methods to analyze the dynamics of those topics. We apply the methodology to two large document collections: 1940 papers from NIPS 00-13 (1987-2000) and 2074 papers from NIPS 14-23 (2001-2010). Six experiments are conducted on the two corpora and the experimental results show that our method is better than LDA in finding research topics and is feasible in discovering the dynamics of research topics.
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