数字人文的文学阐释:情感计算、数字信任及基本实现手段  

Literary Interpretation of Digital Humanities:Affective Computing,Digital Trust,and Basic Implementation Methods

在线阅读下载全文

作  者:卓今[1] ZHUO Jin

机构地区:[1]湖南省社会科学院文学研究所,湖南长沙410003

出  处:《湖南师范大学社会科学学报》2025年第2期141-148,共8页Journal of Social Science of Hunan Normal University

基  金:国家社会科学基金重点项目“文学阐释学的基本概念及理论建构研究”(22AZW002)。

摘  要:数字时代正在改变传统文学研究的资料获取方式和文本感受方式,人们利用数据库、采用“远读方法”研究文学日益普遍。数字人文已经深度介入当代文学批评,但要达到与传统研究方法融合增益,还需要打破情感计算的局限、拓展算法和模型训练、发现美学和艺术表现力以及其背后隐藏的思想价值。文本预处理后存在各种不确定性环节,如何兼顾数字确定性与文学灵动性,需要构建一套方法,如采用情感词典、文学化数据标注员、多模态的“计算诗学”等新的文学研究范式。数字人文与当代文学阐释的深度融合应该有一个任务:在海量的当代文学作品中,AI帮助人类判断哪些是好作品,哪些是值得阅读的作品,与大众判断、批评家判断形成互补。The digital age has transformed the way literary research accesses materials and experiences texts,leading people to rely on databases and“distant reading”methods.Digital humanities has become deeply integrated into contemporary literary criticism.However,to fully integrate and enhance traditional research methods,it is necessary to overcome the limitations of affective computing,expand algorithmic models and training,and uncover the aesthetic and artistic expressiveness,as well as the underlying intellectual values of literature.After preprocessing,texts havevarious uncertainties,raising the challenge of balancing digital determinacy with literary fluidity.This necessitates the development of a new methodological framework,incorporating approaches such as sentiment lexicons,literary data annotators,and multimodal“computational poetics”.The deep integration of digital humanities and contemporary literary interpretation should pursue a central goal:leveraging AI to assist humans in evaluating contemporary literary works,distinguishing which texts are of high quality and worth reading,and complementing assessments made by the general public and literary critics.

关 键 词:数字人文 文学阐释 情感计算 数字信任 文学化数据标注员 

分 类 号:I20[文学—中国文学]

 

参考文献:

正在载入数据...

 

二级参考文献:

正在载入数据...

 

耦合文献:

正在载入数据...

 

引证文献:

正在载入数据...

 

二级引证文献:

正在载入数据...

 

同被引文献:

正在载入数据...

 

相关期刊文献:

正在载入数据...

相关的主题
相关的作者对象
相关的机构对象