標題: A two-step deep learning approach to data classification and modeling and a demonstration on subject type relationship analysis in the Web of Science
作者: Phoa, Frederick Kin Hing
Lai, Hsin-Yi
Chang, Livia Lin-Hsuan
Honda, Keisuke
統計學研究所
Institute of Statistics
關鍵字: Deep learning;Multilayer perceptron;Classification;Web of Science;Dependency
公開日期: 1-Jan-1970
摘要: It is common sense that some subjects have strong relationships while others are perhaps almost mutually independent, but a quantitative and systematic approach to describe such sense is a deficiency. A technique called pointwise mutual information (PMI) from information science helps to fulfill the request, but the calculation through a large-scale database is computationally infeasible if one requires an instantaneous value. This work provides a two-step remedy via deep learning for estimating and predicting relationships among two subject types that are found in the large-scale citation database called the Web of Science. The resulting model successfully replicates existing PMI values among subject types, and it can be used for predicting PMI values of two subject types if one or both subject types does not exist in the database.
URI: http://dx.doi.org/10.1007/s11192-020-03599-y
http://hdl.handle.net/11536/155481
ISSN: 0138-9130
DOI: 10.1007/s11192-020-03599-y
期刊: SCIENTOMETRICS
起始頁: 0
結束頁: 0
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