文章基本信息

标题：Environmental Affection-Driven English Tense Analysis: A Healthcare Exercise-Based Corpus Case Study over Public English Environment
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作者：Yiling Ding ; Tianhua Wang
期刊名称：Journal of Environmental and Public Health
印刷版ISSN：1687-9805
电子版ISSN：1687-9813
出版年度：2022
卷号：2022
DOI：10.1155/2022/9497554
语种：English
出版社：Hindawi Publishing Corporation
摘要：Most international academic papers are written in English, and the use of tenses in English academic papers often follows some conventional rules. Automatically extracting and analyzing English tenses in scientific papers have begun to attract researchers' attention for the global environment. In the analysis of the English tense of scientific papers, consider that the neural network model that combines attention mechanism and sequential input network such as Long Short-Term Memory (LSTM) network has a long training time, low extraction accuracy, and cannot parallelize text input. We propose an environmental affection-driven English tense analysis model, which includes an attention mechanism and LSTM model and conducts a temporal analysis of English texts based on an affective computing model. In this paper, our proposed method is verified based on the self-built healthcare exercise-based corpus over public English environment. By comparison, the experimental results show that the method proposed in this paper has better performance than ordinary Convolutional Neural Network (CNN), Support Vector Machine (SVM), and LSTM based on attention mechanism.