Machine learning in computational literary studies
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.
- Location
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Deutsche Nationalbibliothek Frankfurt am Main
- Extent
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Online-Ressource
- Language
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Englisch
- Bibliographic citation
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Machine learning in computational literary studies ; volume:65 ; number:4-5 ; year:2023 ; pages:200-217 ; extent:18
Information technology ; 65, Heft 4-5 (2023), 200-217 (gesamt 18)
- Creator
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Hatzel, Hans Ole
Stiemer, Haimo
Biemann, Chris
Gius, Evelyn
- DOI
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10.1515/itit-2023-0041
- URN
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urn:nbn:de:101:1-2023112213062515817347
- Rights
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Open Access; Der Zugriff auf das Objekt ist unbeschränkt möglich.
- Last update
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15.08.2025, 7:30 AM CEST
Data provider
Deutsche Nationalbibliothek. If you have any questions about the object, please contact the data provider.
Associated
- Hatzel, Hans Ole
- Stiemer, Haimo
- Biemann, Chris
- Gius, Evelyn
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