NDLI: Research on document similarity calculation and detection based on deep learning

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Research on document similarity calculation and detection based on deep learning

Content Provider	Scilit
Author	Xing, Cui Yang, Yan Luo, Jian
Copyright Year	2021
Description	Journal: Journal of Physics: Conference Series In view of the complexity of traditional document similarity calculation methods and the problem of large error, a document similarity calculation and detection method based on deep learning is proposed. The effective subtree matching method is used to calculate the similarity of document feature sequences, and the frequency of feature items is obtained. In order to reduce the complexity of document similarity detection, a deep learning method is used to classify documents. The keywords in the document are extracted, and the similarity calculation and detection results are obtained by calculating the similarity between keywords. The experimental results show that the calculation error of the proposed method is low and the results are reliable, which fully shows that the method is effective and feasible.
Related Links	https://iopscience.iop.org/article/10.1088/1742-6596/1757/1/012007/pdf
ISSN	17426588
e-ISSN	17426596
DOI	10.1088/1742-6596/1757/1/012007
Journal	Journal of Physics: Conference Series
Issue Number	1
Volume Number	1757
Language	English
Publisher	IOP Publishing
Publisher Date	2021-01-01
Access Restriction	Open
Subject Keyword	Journal: Journal of Physics: Conference Series Hardware and Architecture Similarity Calculation and Detection Deep Learning Document Similarity Calculation
Content Type	Text
Resource Type	Article
Subject	Physics and Astronomy

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