References
- Brottrager, J., Stahl, A., Arslan, A., Brandes, U., & Weitin, T. (2022). Modeling and Predicting Literary Reception. A Data-Rich Approach to Literary Historical Reception. Journal of Computational Literary Studies, 1(1). 10.48694/jcls.95
- Gius, E., Guhr, S., & Uglanova, I. (2021). “d-Prose 1870–1920” a Collection of German Prose Texts from 1870 to 1920. Journal of Open Humanities Data, 7(11). 10.5334/johd.30
- Grisot, G., & Herrmann, B. (2023). Examining the representation of landscape and its emotional value in German-Swiss fiction between 1840 and 1940. Journal of Cultural Analytics, 8(1). 10.22148/001c.84475
- Guhr, S., Monaco, J., Sherman, A., Warner, M., & Algee-Hewitt, M. (2025). Making BERT Feel at Home. Modelling Domestic Space in 19th-Century British and Irish Fiction. Journal of Computational Literary Studies, 4(1). 10.48694/jcls.4164
- Herrmann, J. B., & Lauer, G. (2017).
Das „Was-bisher-geschah“ von KOLIMO: Ein Update zum Korpus der literarischen Moderne . In DHd 2017: Digitale Nachhaltigkeit. Konferenzabstracts (pp. 107–110). Bern: Universität Bern. - Honnibal, M., Montani, I., Van Landeghem, S., & Boyd, A. (2020). spaCy: Industrial-strength natural language processing in Python. 10.5281/zenodo.1212303
- Horstmann, J. (2024). Ressourcenbeitrag: Korpus der literarischen Moderne (KOLIMO). forTEXT, 1(2). 10.48694/fortext.3813
- Jiang, M., Hu, Y., Worthey, G., Dubnicek, R. C., Capitanu, B., Kudeki, D., & Downie, J. S. (2021). The Gutenberg-HathiTrust parallel corpus: A real-world dataset for noise investigation in uncorrected OCR texts. iConference 2021, virtual.
https://www.ideals.illinois.edu/items/117404 - Piper, A. (2023). What do characters do? The embodied agency of fictional characters. Journal of Computational Literary Studies, 2(1), 1–12. 10.48694/jcls.3589
- Pohl, S., & Umlauf, K. (2007). Warenkunde Buch: Strukturen, Inhalte und Tendenzen des deutschsprachigen Buchmarkts der Gegenwart. Wiesbaden: Harrrassowitz Verlag.
- Projekt Gutenberg-DE. (2025). Projekt Gutenberg-DE (2nd ed.).
https://www.projekt-gutenberg.org/ - Qi, P., Zhang, Y., Zhang, Y., Bolton, J., & Manning, C. D. (2020).
Stanza: A Python natural language processing toolkit for many human languages . In Proceedings of the Association for Computational Linguistics (ACL) System Demonstrations (pp. 101–108). Association for Computational Linguistics. 10.18653/v1/2020.acl-demos.14 - Radak, T., Burnard, L., François, P., Hilger, A., Jannidis, F., Palkó, G., Patras, R., Preminger, M., Santos, D., & Schöch, C. (2024). Towards a computational history of modernism in European literary history: Mapping the inner lives of characters in the European novel (1840–1920). Open Research Europe, 4, 44. 10.12688/openreseurope.16290.2
- Rohrbacher, K. (forthcoming).
“Lived space”: A computational study of setting in fiction . In R. M. Aust, G. Grisot, & B. Herrmann (Eds.), Comparing landscapes: Approaches to space and affect in literary fiction. Bielefeld University Press. - Rohrbacher, K. (2025). Opening worlds: Narrative beginnings and the role of setting. CCLS2025 Conference Preprints, 4(1). 10.26083/tuprints-00030149
- Schöch, C., Erjavec, T., Patras, R., & Santos, D. (2021). Creating the European literary text collection (ELTeC): Challenges and perspectives. Modern Languages Open, 0(1), 25. 10.3828/mlo.v0i0.364
- Underwood, T., Kimutis, P., & Witte, J. (2020). NovelTM datasets for English-language fiction, 1700–2009. Journal of Cultural Analytics, 5(2). 10.22148/001c.13147
- Wilkens, M. (2021). Too isolated, too insular: American literature and the world. Journal of Cultural Analytics, 6(3), 52–84. 10.22148/001c.25273
