目的:提出一种基于多特征融合的中医药问题生成模型(MFFQG),以改善现有的自动生成技术在处理特定领域时存在的领域关键词信息缺失和生成问题表达不规范问题。方法:利用RoBERTa向量和五笔向量捕捉输入序列的语义特征和字形特征,同时融合...目的:提出一种基于多特征融合的中医药问题生成模型(MFFQG),以改善现有的自动生成技术在处理特定领域时存在的领域关键词信息缺失和生成问题表达不规范问题。方法:利用RoBERTa向量和五笔向量捕捉输入序列的语义特征和字形特征,同时融合句法信息和所构建的中医药领域主副关键词信息,将得到的多特征向量信息送入UniLM生成模型得到生成结果,实现对中医药领域问题的自动生成。结果:MFFQG模型融合多种特征,在Rouge-1、Rouge-2、Rouge-L评价指标上分别达到64.93%、34.57%、63.05%。局限:数据主要来源于中医药领域,在其他领域中的效果有待验证。结论:MFFQG模型相较于对比模型,可以显著提升中医药问题的生成质量。Objective: To propose a traditional Chinese medicine problem generation model (MFFQG) based on multi feature fusion, in order to improve the problems of missing domain keyword information and non-standard expression of generation problems in existing automatic generation technologies when dealing with specific fields. Method: Using RoBERTa vectors and Wubi vectors to capture the semantic and glyph features of the input sequence, while integrating syntactic information and the constructed main and auxiliary keyword information in the field of traditional Chinese medicine, the obtained multi feature vector information is fed into the UniLM generation model to obtain the generated results, achieving automatic generation of problems in the field of traditional Chinese medicine. Result: The MFFQG model integrates multiple features and achieves 64.93%, 34.57%, and 63.05% in Rouge-1, Rouge-2, and Rouge-L evaluation indicators, respectively. Limitation: The data mainly comes from the field of traditional Chinese medicine, and its effectiveness in other fields needs to be verified. Conclusion: Compared to the comparative model, the MFFQG model can significantly improve the quality of generating traditional Chinese medicine problems.展开更多
基于可视化探析我国高等理科教育,从文献中研究目前高等理科教育现状,并结合中医药类高校进行讨论。该研究以中国知网(CNKI)文献总库作为文献检索源,使用CiteSpace对与主题“理科”和(AND)“高等教育”相关的文献进行可视化,并使用Exce...基于可视化探析我国高等理科教育,从文献中研究目前高等理科教育现状,并结合中医药类高校进行讨论。该研究以中国知网(CNKI)文献总库作为文献检索源,使用CiteSpace对与主题“理科”和(AND)“高等教育”相关的文献进行可视化,并使用Excel辅助制作表格,总共呈现的知识图谱有:发文趋势图、关键词共现图、高频关键词表格、关键词聚类统计表格、关键词时序演进图、关键词突现图。并对其进行可视化定量研究,深入探讨其研究热点和未来趋势,结合中医药类高校以探析中医药类高校理科教育情况,期望对新时代发展中医药特色高校发展有所启发。Based on a visual exploration of higher education in the sciences in China, this study examines the current state of higher education in the sciences through literature research and discusses it in conjunction with universities specializing in traditional Chinese medicine. The research uses the China National Knowledge Infrastructure (CNKI) literature database as the source for literature retrieval, employing CiteSpace to visualize literature related to the themes of “science” and “higher education”. Excel is used to assist in creating tables. The knowledge maps presented include: publication trend graphs, keyword co-occurrence maps, high-frequency keyword tables, keyword clustering statistics tables, keyword temporal evolution graphs, and keyword burst graphs. A quantitative visual analysis is conducted to explore research hotspots and future trends, with a focus on the situation of science education in traditional Chinese medicine universities, aiming to provide insights into the development of universities with traditional Chinese medicine characteristics in the new era.展开更多
文摘目的:提出一种基于多特征融合的中医药问题生成模型(MFFQG),以改善现有的自动生成技术在处理特定领域时存在的领域关键词信息缺失和生成问题表达不规范问题。方法:利用RoBERTa向量和五笔向量捕捉输入序列的语义特征和字形特征,同时融合句法信息和所构建的中医药领域主副关键词信息,将得到的多特征向量信息送入UniLM生成模型得到生成结果,实现对中医药领域问题的自动生成。结果:MFFQG模型融合多种特征,在Rouge-1、Rouge-2、Rouge-L评价指标上分别达到64.93%、34.57%、63.05%。局限:数据主要来源于中医药领域,在其他领域中的效果有待验证。结论:MFFQG模型相较于对比模型,可以显著提升中医药问题的生成质量。Objective: To propose a traditional Chinese medicine problem generation model (MFFQG) based on multi feature fusion, in order to improve the problems of missing domain keyword information and non-standard expression of generation problems in existing automatic generation technologies when dealing with specific fields. Method: Using RoBERTa vectors and Wubi vectors to capture the semantic and glyph features of the input sequence, while integrating syntactic information and the constructed main and auxiliary keyword information in the field of traditional Chinese medicine, the obtained multi feature vector information is fed into the UniLM generation model to obtain the generated results, achieving automatic generation of problems in the field of traditional Chinese medicine. Result: The MFFQG model integrates multiple features and achieves 64.93%, 34.57%, and 63.05% in Rouge-1, Rouge-2, and Rouge-L evaluation indicators, respectively. Limitation: The data mainly comes from the field of traditional Chinese medicine, and its effectiveness in other fields needs to be verified. Conclusion: Compared to the comparative model, the MFFQG model can significantly improve the quality of generating traditional Chinese medicine problems.
文摘基于可视化探析我国高等理科教育,从文献中研究目前高等理科教育现状,并结合中医药类高校进行讨论。该研究以中国知网(CNKI)文献总库作为文献检索源,使用CiteSpace对与主题“理科”和(AND)“高等教育”相关的文献进行可视化,并使用Excel辅助制作表格,总共呈现的知识图谱有:发文趋势图、关键词共现图、高频关键词表格、关键词聚类统计表格、关键词时序演进图、关键词突现图。并对其进行可视化定量研究,深入探讨其研究热点和未来趋势,结合中医药类高校以探析中医药类高校理科教育情况,期望对新时代发展中医药特色高校发展有所启发。Based on a visual exploration of higher education in the sciences in China, this study examines the current state of higher education in the sciences through literature research and discusses it in conjunction with universities specializing in traditional Chinese medicine. The research uses the China National Knowledge Infrastructure (CNKI) literature database as the source for literature retrieval, employing CiteSpace to visualize literature related to the themes of “science” and “higher education”. Excel is used to assist in creating tables. The knowledge maps presented include: publication trend graphs, keyword co-occurrence maps, high-frequency keyword tables, keyword clustering statistics tables, keyword temporal evolution graphs, and keyword burst graphs. A quantitative visual analysis is conducted to explore research hotspots and future trends, with a focus on the situation of science education in traditional Chinese medicine universities, aiming to provide insights into the development of universities with traditional Chinese medicine characteristics in the new era.
