文献类型：期刊文献

中文题名：跨学科语义漂移识别与可视化分析

英文题名：Recognition and Visual Analysis of Interdisciplinary Semantic Drift

作者：李楠[1];汪波[1]

机构：[1]华东理工大学科技信息研究所,上海200237

年份：2023

卷号：7

期号：10

起止页码：15

中文期刊名：数据分析与知识发现

外文期刊名：Data Analysis and Knowledge Discovery

收录：CSTPCD;;国家哲学社会科学学术期刊数据库;EI(收录号：20240415417866);Scopus;北大核心:【北大核心2020】；CSSCI:【CSSCI2023_2024】；CSCD:【CSCD_E2023_2024】；

基金：中央高校基本科研业务费专项资金项目(项目编号:222202226002)的研究成果之一。

语种：中文

中文关键词：语义漂移;文本分析;BERT-Whitening

外文关键词：Semantic Drift;Textual Analysis;BERT-Whitening

摘要：【目的】借助机器学习技术分析并呈现领域术语的语义漂移现象,实现跨学科语义漂移识别与可视化,挖掘语义漂移的规律及成因。【方法】结合深度学习方法,设计一种领域术语语义漂移识别与可视化框架,该框架采用“SBERT模型+词嵌入优化+层次聚类”的组合算法实现跨学科语义漂移识别,综合Bokeh、主成分分析法对跨学科语义漂移现象进行可视化展示。【结果】所提方法能够准确识别跨学科语义漂移,在DTSentence数据集上的整体识别精确率达到86.15%。【局限】技术框架的普适性尚未得到验证,后续研究将拓展其在不同学科领域中的应用。【结论】所提方法有利于语义漂移规律的挖掘及可视化,为语义演化、语义理解、语义建模等研究奠定良好的技术基础。
[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.