A two-step deep learning approach to data classification and modeling and a demonstration on subject type relationship analysis in the Web of Science.

Autor/inn/en	Phoa, Frederick Kin Hing; Lai, Hsin-Yi; Chang, Livia Lin-Hsuan; Honda, Keisuke
Titel	A two-step deep learning approach to data classification and modeling and a demonstration on subject type relationship analysis in the Web of Science.
Quelle	In: Scientometrics, (2020) 2, S.851-863 PDF als Volltext Verfügbarkeit
Dokumenttyp	gedruckt; online; Zeitschriftenaufsatz
ISSN	0138-9130
DOI	10.1007/s11192-020-03599-y
Schlagwörter	Deep learning; Multilayer perceptron; Classification; Web of Science; Dependency
Abstract	Abstract 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.
Erfasst von	OLC
Update	2023/2/05