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| Recognition and Visual Analysis of Interdisciplinary Semantic Drift |
Li Nan,Wang Bo( ) |
| Institute of Science and Technology Information, East China University of Science and Technology, Shanghai 200237, China |
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Abstract [Objective] This paper analyzes the semantic drift of domain terms with machine learning techniques. It recognizes and visualizes interdisciplinary semantic drifts and explores their patterns and causes. [Methods] We designed a framework for identifying and visualizing the semantic drift of domain terms with deep learning algorithms. The framework combined algorithms of “SBERT model+word embedding optimization+hierarchical clustering” to identify interdisciplinary semantic drift. It also utilized Bokeh and principal component analysis to visualize the phenomenon of interdisciplinary semantic drift. [Results] The proposed framework can accurately identify interdisciplinary semantic drift, and the overall recognition accuracy (p) in the DT-Sentence dataset reached 86.15%. [Limitations] The framework needs to be verified with more disciplines’ datasets. [Conclusions] This study benefits data mining and visualization of semantic drifts. It also lays the technical foundation for semantic evolution, understanding, and modeling.
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Received: 19 June 2022
Published: 22 March 2023
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| Fund:Fundamental Research Funds for the Central Universities of Ministry of Education of China(222202226002) |
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Corresponding Authors:
Wang Bo，ORCID：0000-0001-5222-950X，E-mail：wang.bo.tianwen@qq.com。
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