References
- Bakhtin, M. M. (1981). The dialogic imagination: Four essays. (M. Holquist, Ed.; C. Emerson & M. Holquist, Trans.). University of Texas Press.
- Barré, J. (2024). Latent Structures of intertextuality in French fiction: How literary recognition and subgenres are framing textuality. arXiv:2410.17759. 10.48550/arXiv.2410.17759
- Cer, D., Diab, M., Agirre, E., Lopez-Gazpio, I., & Specia, L. (2017). SemEval-2017 Task 1: Semantic Textual Similarity Multilingual and Crosslingual Focused Evaluation. Proceedings of the 11th International Workshop on Semantic Evaluation (SemEval-2017), 1–14. 10.18653/v1/S17-2001
- Chaudhary, P., & Dexter, J. (2023). Intertextuality: Computational tools for identifying related passages in large corpora. Quantitative Criticism Lab.
https://www.qcrit.org/research/intertextuality (last accessed: 10 November 2025). - Cochran, P. (2009). Byron’s Works. Peter Cochran’s Website – Film Reviews, Poems, Byron… Web.
https://petercochran.wordpress.com/byron-2/byrons-works (last accessed: 08 January 2026). - Coffee, N., Koenig, J. P., Poornima, S., Forstall, C., Ossewaarde, R., & Jacobson, S. (2012). The tesserae project: Intertextual analysis of Latin poetry. Literary and Linguistic Computing, 28, 221–228. 10.1093/llc/fqs033
- Cooney, C., Horton, R., Olsen, M., Roe, G., & Voyer, R. (2008). Hidden Roads and Twisted Paths: Intertextual discovery using clusters, classifications, and similarities. Digital Humanities 2008 Book of Abstracts, 93–94.
https://openresearch-repository.anu.edu.au/bitstreams/19494939-20e2-43bf-be64-a264d770889a/download (last accessed: 06 January 2026). - Duan, S. (2025). Quantitative intertextuality from the digital humanities perspective: A survey. arXiv:2510.27045. 10.48550/arXiv.2510.27045
- Fodor, J., De Deyne, S., & Suzuki, S. (2025). Compositionality and sentence meaning: Comparing semantic parsing and transformers on a challenging sentence similarity dataset. Computational Linguistics, 51(1), 139–190. 10.1162/coli_a_00536
- Forstall, C. W., & Scheirer, W. J. (2019). Quantitative intertextuality: Analyzing the markers of information reuse. Cham: Springer International Publishing AG. 10.1007/978-3-030-23415-7
- Genette, G. (with Prince, G.). (1997). Palimpsests: Literature in the second degree (C. Newman & C. Doubinsky, Trans.). University of Nebraska Press. (Original work published 1982)
- Goel, A. (2025). LangExtract (Version 1.1.1) [Computer software]. 10.5281/zenodo.17015089
- Goodman, P. (1954). The Structure of Literature. University of Chicago Press.
- Guerra, R. (2023). From physics to data science: The beauty and power of cosine similarity. Medium.
https://medium.com/@rgalvg/from-physics-to-data-science-the-beauty-and-power-of-cosine-similarity-f23e276afe29 (last accessed: 11 January 2026). - Hinds, S. (1998). Allusion and Intertext: Dynamics of Appropriation in Roman Poetry. Cambridge University Press.
- Horton, R., Olsen, M., & Roe, G. (2010). Something borrowed: Sequence alignment and the identification of similar passages in large text collections. Digital Studies/Le champ numérique, 2(1).
http://hdl.handle.net/1885/12104 - Hume, D. (1739). A Treatise of Human Nature. Hume Texts Online.
https://davidhume.org/texts/t/1/1/4 (last accessed: 3 February 2026). - Johnson, N., Bertsch, A., Deal, M-E., & Strubell, E. (2025). FicSim: A Dataset for Multi-Faceted Semantic Similarity in Long-Form Fiction. Findings of the Association for Computational Linguistics: EMNLP 2025, 25228–25246. 10.18653/v1/2025.findings-emnlp.1375
- Joshi, B., Shah, N., Barbieri, F., & Neves, L. (2020). The Devil is in the Details: Evaluating Limitations of Transformer-based Methods for Granular Tasks. Proceedings of the 28th International Conference on Computational Linguistics, 3652–3659. 10.18653/v1/2020.coling-main.326
- Khandelwal, U., He, H., Qi, P., & Jurafsky, D. (2018). Sharp nearby, fuzzy far away: How neural language models use context. Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), 284–294. 10.18653/v1/P18-1027
- Kristeva, J. (1981). Desire in language: A semiotic approach to literature and art (T. Gora, A. Jardine, & L. S. Roudiez, Trans., L. S. Roudiez, Ed.). Basil Blackwell. (Original work published 1977)
- Kuznetsov, I., Buchmann, J., Eichler, M., & Gurevych, I. (2022). Revise and Resubmit: An intertextual model of text-based collaboration in peer review. Computational Linguistics; 48(4), 949–986. 10.1162/coli_a_00455
- Lau, P. K., & McManus S. M, (2024). Mining asymmetric intertextuality. arXiv:2410.15145. 10.48550/arXiv.2410.15145
- Losses. SBERT.net.
https://sbert.net/docs/package_reference/sentence_transformer/losses.html (last accessed: 11 January 2026). - Luo, M., Kumbhar, S., Shen, M., Parmar, M., Varshney, N., Banerjee, P., Aditya, S., & Baral, C. (2024). Towards LogiGLUE: A brief survey and a benchmark for analyzing logical reasoning capabilities of language models. arXiv:2310.00836v3. 10.48550/arXiv.2310.00836
- Mahadevan, A., Mathioudakis, M., Mäkelä, E., & Tolonen, M. (2025). Text reuse in large historical corpora: Insights from the optimization of a data science system. International Journal of Data Science and Analytics, 20(5), 4631–4643. 10.1007/s41060-025-00742-x
- May, P. (2021). Machine translated multilingual STS benchmark dataset.
https://github.com/PhilipMay/stsb-multi-mt (last accessed: 09 January 2026). - Miller, H., Kuflik, T., & Lavee, M. (2025). Text Alignment in the Service of Text Reuse Detection. Applied Sciences, 15(6),
3395 . 10.3390/app15063395 - Peng, B., Narayanan, S., & Papadimitriou, C. (2024). On limitations of the transformer architecture. arXiv:2402.08164v2. 10.48550/arXiv.2402.08164
- Press, O., Zhang, M., Min, S., Schmidt, L., Smith, N., & Lewis, M. (2023). Measuring and narrowing the compositionality gap in language models. Findings of the Association for Computational Linguistics: EMNLP 2023, 5687–5711. 10.18653/v1/2023.findings-emnlp.378
- Ramsay, S. (2011). Reading machines: Toward an Algorithmic Criticism. University of Illinois Press. 10.16995/dscn.245
- Reimers, N., & Gurevych, I. (2019). Sentence-BERT: Sentence embeddings using Siamese BERT-networks. Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), 3982–3992. 10.18653/v1/D19-1410
- Roe, G., Gladstone, C., & Morrissey, R. (2016). Discourses and disciplines in the enlightenment: Topic modeling the french encyclopédie. Frontiers in Digital Humanities, 2. 10.3389/fdigh.2015.00008
- Romanello, M. (2016). Exploring Citation Networks to Study Intertextuality in Classics. Digital Humanities Quarterly, 10(2).
- Semantic Search. SBERT.net.
https://www.sbert.net/examples/sentence_transformer/applications/semantic-search/README.html (last accessed: 11 January 2026). - Scheirer, W., Forstall, C., & Coffee, N. (2016). The sense of a connection: Automatic tracing of intertextuality by meaning, Digital Scholarship in the Humanities, 31(1), 204–217. 10.1093/llc/fqu058
- Schubert, C. (2020). Intertextuality and Digital Humanities. it – Information technology, 62(2), 53–59. 10.1515/itit-2019-0036
- Smiley, D. M. (2025). Intertextual parallel detection in Biblical Hebrew: A transformer-based benchmark. arXiv:2506.24117. 10.48550/arXiv.2506.24117
- Stabler, J., & Hopps, G. (2024). The poems of Lord Byron – Don Juan (Vol. 4 & 5). Routledge. 10.4324/9781003571087
- Steyer, K. (2015). Irgendwie hängt alles mit allem zusammen – grenzen und möglichkeiten einer linguistischen kategorie ‘intertextualität’. Textbeziehungen. Linguistische und literaturwissenschaftliche Beiträge zur Intertextualität, 83 – 106.
- Sui, P., Rodriguez, J. D., Laban, P., Murphy, J. D., Dexter, J. P., So, R. J., Baker, S., & Chaudhuri, P. (2025). KRISTEVA: Close Reading as a novel task for benchmarking interpretive reasoning. Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), 32829–32849. 10.18653/v1/2025.acl-long.1577
- Takahashi, H., Lu, X., Ishijima, S., Seo, D., Kim, T., Park, S., Song, M., Marante, K., Iso, K.,Tokura, H., & Ohman, E. (2024). OZemi at SemEval-2024 Task 1: A Simplistic Approach to Textual Relatedness Evaluation Using Transformers and Machine Translation. Proceedings of the 18th International Workshop on Semantic Evaluation (SemEval-2024), 7–12, 10.18653/v1/2024.semeval-1.2
- Trillini, R. H., & Quassdorf, S. (2010). A ‘key to all quotations’? A corpus-based parameter model of intertextuality, Literary and Linguistic Computing, 25(3), 269–286. 10.1093/llc/fqq003
- Underwood, T. (2019). Distant horizons: Digital evidence and literary change. Chicago: The University of Chicago Press. 10.7208/chicago/9780226612973.001.0001
- Xing, Y. (2025). Modelling intertextuality with n-gram embeddings. arXiv:2509.06637. 10.48550/arXiv.2509.06637
