Machine learning in computational literary studies

IF 1 Q4 COMPUTER SCIENCE, INFORMATION SYSTEMS
Hans Ole Hatzel, Haimo Stiemer, Chris Biemann, Evelyn Gius
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引用次数: 0

Abstract

Abstract In this article, we provide an overview of machine learning as it is applied in computational literary studies, the field of computational analysis of literary texts and literature related phenomena. We survey a number of scientific publications for the machine learning methodology the scholars used and explain concepts of machine learning and natural language processing while discussing our findings. We establish that besides transformer-based language models, researchers still make frequent use of more traditional, feature-based machine learning approaches; possible reasons for this are to be found in the challenging application of modern methods to the literature domain and in the more transparent nature of traditional approaches. We shed light on how machine learning-based approaches are integrated into a research process, which often proceeds primarily from the non-quantitative, interpretative approaches of non-digital literary studies. Finally, we conclude that the application of large language models in the computational literary studies domain may simplify the application of machine learning methodology going forward, if adequate approaches for the analysis of literary texts are found.
计算机文学研究中的机器学习
摘要在这篇文章中,我们概述了机器学习在计算文学研究、文学文本的计算分析领域和文学相关现象中的应用。我们调查了学者们使用的机器学习方法的一些科学出版物,并在讨论我们的发现时解释了机器学习和自然语言处理的概念。我们发现,除了基于转换器的语言模型外,研究人员仍然经常使用更传统的、基于特征的机器学习方法;这可能的原因在于现代方法在文学领域的挑战性应用,以及传统方法更透明的性质。我们揭示了基于机器学习的方法是如何融入研究过程的,研究过程通常主要来自非数字文学研究的非定量、解释性方法。最后,我们得出结论,如果找到足够的文学文本分析方法,大型语言模型在计算文学研究领域的应用可能会简化机器学习方法的应用。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
IT-Information Technology
IT-Information Technology COMPUTER SCIENCE, INFORMATION SYSTEMS-
CiteScore
3.80
自引率
0.00%
发文量
29
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