Browsing by Author "Honda, Keisuke"

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A Deep-Learning Approach to Determine the Dependency between Two Subject Types in the Web of Science
(2019-01-01)
Phoa, Frederick Kin Hing
;
Lai, Hsin-Yi
;
Chang, Livia Lin-Hsuan
;
Honda, Keisuke
;
統計學研究所
Traditional wisdom always suggests that some subjects have strong relationships, while others are almost mutual independent. However. there is a lack of quantitative approach to formulate these relationships in a systematic and unbiased way. In this work, we train a classification machine via deep learning to determine whether two subject types are independent based on the citation information from the Web of Science database. This machine not only achieves very high accuracy in estimating the dependency among subject types in the database, but also is able to predict the dependency when one or both subject types do not exist in the database.
A two-step deep learning approach to data classification and modeling and a demonstration on subject type relationship analysis in the Web of Science
(1970-01-01)
Phoa, Frederick Kin Hing
;
Lai, Hsin-Yi
;
Chang, Livia Lin-Hsuan
;
Honda, Keisuke
;
統計學研究所
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.