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Research on Methodology of Correlation Analysis of Sci-Tech Literature Based on Deep Learning Technology in the Big Data

Research on Methodology of Correlation Analysis of Sci-Tech Literature Based on Deep Learning Technology in the Big Data

Wen Zeng, Hongjiao Xu, Hui Li, Xiang Li
Copyright: © 2018 |Volume: 29 |Issue: 3 |Pages: 22
ISSN: 1063-8016|EISSN: 1533-8010|EISBN13: 9781522542261|DOI: 10.4018/JDM.2018070104
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MLA

Zeng, Wen, et al. "Research on Methodology of Correlation Analysis of Sci-Tech Literature Based on Deep Learning Technology in the Big Data." JDM vol.29, no.3 2018: pp.67-88. http://doi.org/10.4018/JDM.2018070104

APA

Zeng, W., Xu, H., Li, H., & Li, X. (2018). Research on Methodology of Correlation Analysis of Sci-Tech Literature Based on Deep Learning Technology in the Big Data. Journal of Database Management (JDM), 29(3), 67-88. http://doi.org/10.4018/JDM.2018070104

Chicago

Zeng, Wen, et al. "Research on Methodology of Correlation Analysis of Sci-Tech Literature Based on Deep Learning Technology in the Big Data," Journal of Database Management (JDM) 29, no.3: 67-88. http://doi.org/10.4018/JDM.2018070104

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Abstract

In the big data era, it is a great challenge to identify high-level abstract features out of a flood of sci-tech literature to achieve in-depth analysis of data. The deep learning technology has developed rapidly and achieved applications in many fields, but has rarely been utilized in the research of sci-tech literature data. This article introduced the presentation method of vector space of terminologies in sci-tech literature based on the deep learning model. It explored and adopted a deep AE model to reduce the dimensionality of input word vector feature. Also put forward is the methodology of correlation analysis of sci-tech literature based on deep learning technology. The experimental results showed that the processing of sci-tech literature data could be simplified into the computation of vectors in the multi-dimensional vector space, and the similarity in vector space could be used to represent similarity in text semantics. The correlation analysis of subject contents between sci-tech literatures of the same or different types can be made using this method.

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