新媒介的出现使传统印刷文化遭受了巨大冲击,而近年来随着深度神经网络技术的发展,生成式人工智能的学习能力产生了质的飞跃,也在创作实践中塑造了新的作者形式。与以往文学史中的任何创作形式都不同,人机合作的创作模式是一种“1+n”...新媒介的出现使传统印刷文化遭受了巨大冲击,而近年来随着深度神经网络技术的发展,生成式人工智能的学习能力产生了质的飞跃,也在创作实践中塑造了新的作者形式。与以往文学史中的任何创作形式都不同,人机合作的创作模式是一种“1+n”的新形态。然而透过现代主体与后人类主体的冲突,可以看到数字技术并未完全将人类作者改变为“赛博格主体”,当代的作者仍然站在现代文明的大地上;同时,借助英国约克大学教授阿曼达·雷斯(Amanda Rees)的“万物人文主义”与《周易》中“文”的哲学意涵,能够开辟出认识生成式人工智能作为作者主体的一条新思维路径。在美国麻省理工学院教授约瑟夫·卡尔·罗伯内特·利克莱德(Joseph Carl Robnett Licklider)所谓的“人机共生”的命运下,当代的人工智能文学作者与其说是数字时代的创作者,不如说是以一种走向数字时代的姿态进行写作的创作者。展开更多
Objective Natural language processing (NLP) was used to excavate and visualize the core content of syndrome element syndrome differentiation (SESD). Methods The first step was to build a text mining and analysis envir...Objective Natural language processing (NLP) was used to excavate and visualize the core content of syndrome element syndrome differentiation (SESD). Methods The first step was to build a text mining and analysis environment based on Python language, and built a corpus based on the core chapters of SESD. The second step was to digitalize the corpus. The main steps included word segmentation, information cleaning and merging, document-entry matrix, dictionary compilation and information conversion. The third step was to mine and display the internal information of SESD corpus by means of word cloud, keyword extraction and visualization. Results NLP played a positive role in computer recognition and comprehension of SESD. Different chapters had different keywords and weights. Deficiency syndrome elements were an important component of SESD, such as "Qi deficiency""Yang deficiency" and "Yin deficiency". The important syndrome elements of substantiality included "Blood stasis""Qi stagnation", etc. Core syndrome elements were closely related. Conclusions Syndrome differentiation and treatment was the core of SESD. Using NLP to excavate syndromes differentiation could help reveal the internal relationship between syndromes differentiation and provide basis for artificial intelligence to learn syndromes differentiation.展开更多
文摘新媒介的出现使传统印刷文化遭受了巨大冲击,而近年来随着深度神经网络技术的发展,生成式人工智能的学习能力产生了质的飞跃,也在创作实践中塑造了新的作者形式。与以往文学史中的任何创作形式都不同,人机合作的创作模式是一种“1+n”的新形态。然而透过现代主体与后人类主体的冲突,可以看到数字技术并未完全将人类作者改变为“赛博格主体”,当代的作者仍然站在现代文明的大地上;同时,借助英国约克大学教授阿曼达·雷斯(Amanda Rees)的“万物人文主义”与《周易》中“文”的哲学意涵,能够开辟出认识生成式人工智能作为作者主体的一条新思维路径。在美国麻省理工学院教授约瑟夫·卡尔·罗伯内特·利克莱德(Joseph Carl Robnett Licklider)所谓的“人机共生”的命运下,当代的人工智能文学作者与其说是数字时代的创作者,不如说是以一种走向数字时代的姿态进行写作的创作者。
基金the funding support from the National Natural Science Foundation of China (No. 81874429)Digital and Applied Research Platform for Diagnosis of Traditional Chinese Medicine (No. 49021003005)+1 种基金2018 Hunan Provincial Postgraduate Research Innovation Project (No. CX2018B465)Excellent Youth Project of Hunan Education Department in 2018 (No. 18B241)
文摘Objective Natural language processing (NLP) was used to excavate and visualize the core content of syndrome element syndrome differentiation (SESD). Methods The first step was to build a text mining and analysis environment based on Python language, and built a corpus based on the core chapters of SESD. The second step was to digitalize the corpus. The main steps included word segmentation, information cleaning and merging, document-entry matrix, dictionary compilation and information conversion. The third step was to mine and display the internal information of SESD corpus by means of word cloud, keyword extraction and visualization. Results NLP played a positive role in computer recognition and comprehension of SESD. Different chapters had different keywords and weights. Deficiency syndrome elements were an important component of SESD, such as "Qi deficiency""Yang deficiency" and "Yin deficiency". The important syndrome elements of substantiality included "Blood stasis""Qi stagnation", etc. Core syndrome elements were closely related. Conclusions Syndrome differentiation and treatment was the core of SESD. Using NLP to excavate syndromes differentiation could help reveal the internal relationship between syndromes differentiation and provide basis for artificial intelligence to learn syndromes differentiation.
