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Cited by 16Digital Humanities and ScholarshipClassical Studies and PhilologyTopic Modeling

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Profiling of Intertextuality in Latin Literature Using Word Embeddings

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Burns, Patrick J., Brofos, James A., Li, Kyle, Chaudhuri, Pramit, Dexter, Joseph P.·2021·Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies
DOI 10.18653/v1/2021.naacl-main.389
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Patrick J. Burns, James A. Brofos, Kyle Li, Pramit Chaudhuri, Joseph P. Dexter. Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies. 2021.

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Research works printed but not linked: 33

  1. Geoff Bacon. 2020. Data-driven choices in neural part-of-speech tagging for Latin. In Proceedings of LT4HALA 2020 -1st Workshop on Language Technologies for Historical and Ancient Languages, pages 111-113, Marseille. European Language Re- sources Association (ELRA).
  2. David Bamman and Gregory Crane. 2008. The logic and discovery of textual allusion. In Proceedings of the 2008 LREC Workshop on Language Technology for Cultural Heritage Data.
  3. David Bamman and Gregory Crane. 2011. Measuring historical word sense variation. In Proceedings of the 11th Annual International ACM/IEEE Joint Con- ference on Digital Libraries, page 1-10, New York, New York. Association for Computing Machinery. · DOI
  4. David Bamman and David Smith. 2012. Extracting two thousand years of Latin from a million book li- brary. Journal on Computing and Cultural Heritage, 5. · DOI
  5. David Bamman, Ted Underwood, and Noah A. Smith. 2014. A Bayesian mixed effects model of literary character. In Proceedings of the 52nd Annual Meet- ing of the Association for Computational Linguis- tics (Volume 1: Long Papers), pages 370-379, Balti- more, Maryland. Association for Computational Lin- guistics. · DOI
  6. Miruna-Stefania Barbu and Stefan Trausan-Matu. 2017. Intertextuality detection in literary texts using word2vec models. In 2017 21st International Con- ference on System Theory, Control and Computing (ICSTCC), pages 262-265. Institute of Electrical and Electronics Engineers. · DOI
  7. Neil Bernstein, Kyle Gervais, and Wei Lin. 2015. Com- parative rates of text reuse in classical Latin hexam- eter poetry. Digital Humanities Quarterly, 9.
  8. Johannes Bjerva and Raf Praet. 2015. Word embed- dings pointing the way for late antiquity. In Proceed- ings of the 9th SIGHUM Workshop on Language Technology for Cultural Heritage, Social Sciences, and Humanities (LaTeCH), pages 53-57, Beijing. Association for Computational Linguistics. · DOI
  9. Jelke Bloem, Maria Chiara Parisi, Martin Reynaert, Yvette Oortwijn, and Arianna Betti. 2020. Distri- butional semantics for neo-Latin. In Proceedings of LT4HALA 2020 -1st Workshop on Language Technologies for Historical and Ancient Languages, pages 84-93, Marseille. European Language Re- sources Association (ELRA).
  10. Giuseppe Celano. 2020. A gradient boosting-Seq2Seq system for Latin POS tagging and lemmatization. In Proceedings of LT4HALA 2020 -1st Workshop on Language Technologies for Historical and An- cient Languages, pages 119-123, Marseille. Euro- pean Language Resources Association (ELRA).
  11. Pramit Chaudhuri and Joseph P. Dexter. 2017. Bioin- formatics and classical literary study. Journal of Data Mining & Digital Humanities, Special Issue on Computer-Aided Processing of Intertextuality in An- cient Languages.
  12. Neil Coffee, Jean-Pierre Koenig, Shakthi Poornima, Roelant Ossewaarde, Christopher Forstall, and Sarah Jacobson. 2012. Intertextuality in the digital age. Transactions of the American Philological As- sociation, 142:383-422. · DOI
  13. Neil Coffee, Jean-Pierre Koenig, Shakthi Poornima, Roelant Ossewaarde, Christopher Forstall, and Sarah Jacobson. 2013. The Tesserae Project: Inter- textual analysis of Latin poetry. Literary and Lin- guistic Computing, 28:221-228. · DOI
  14. Joseph P. Dexter, Theodore Katz, Nilesh Tripuraneni, Tathagata Dasgupta, Ajay Kannan, James A. Bro- fos, Jorge A. Bonilla Lopez, Lea A. Schroeder, Adriana Casarez, Maxim Rabinovich, Ayelet Haim- son Lushkov, and Pramit Chaudhuri. 2017. Quan- titative criticism of literary relationships. Pro- ceedings of the National Academy of Sciences, 114:E3195-E3204. · DOI
  15. Christopher W. Forstall, Sarah L. Jacobson, and Wal- ter J. Scheirer. 2011. Evidence of intertextuality: in- vestigating Paul the Deacon's Angustae Vitae. Liter- ary and Linguistic Computing, 26:285-296. · DOI
  16. Christopher W. Forstall and Walter J. Scheirer. 2019. Quantitative Intertextuality: Analyzing the Markers of Information Reuse. Springer International Pub- lishing, Cham.
  17. Edouard Grave, Piotr Bojanowski, Prakhar Gupta, Ar- mand Joulin, and Tomas Mikolov. 2018. Learn- ing word vectors for 157 languages. In Proceed- ings of the Eleventh International Conference on Language Resources and Evaluation (LREC 2018), pages 3483-3487, Miyazaki. European Language Resources Association (ELRA).
  18. Stephen E. Hinds. 1998. Allusion and Intertext: Dy- namics of Appropriation in Roman Poetry. Cam- bridge University Press, Cambridge.
  19. Marko Juvan. 2009. History and Poetics of Intertextu- ality. Purdue University Press, West Lafayette, Indi- ana.
  20. Julia Kristeva. 1980. Word, dialogue, and novel. In Leon S. Roudiez, editor, Desire in Language: A Semiotic Approach to Literature and Art, pages 64- 91. Columbia University Press, New York, New York.
  21. John Lee. 2007. A computational model of text reuse in ancient literary texts. In Proceedings of the 45th Annual Meeting of the Association of Computational Linguistics, pages 472-479, Prague. Association for Computational Linguistics.
  22. Enrique Manjavacas, Brian Long, and Mike Kestemont. 2019. On the feasibility of automated detection of allusive text reuse. In Proceedings of the 3rd Joint SIGHUM Workshop on Computational Linguistics for Cultural Heritage, Social Sciences, Humanities and Literature, pages 104-114, Minneapolis, Min- nesota. Association for Computational Linguistics.
  23. Maria Moritz, Andreas Wiederhold, Barbara Pavlek, Yuri Bizzoni, and Marco Büchler. 2016. Non-literal text reuse in historical texts: An approach to identify reuse transformations and its application to Bible reuse. In Proceedings of the 2016 Conference on Empirical Methods in Natural Language Process- ing, pages 1849-1859, Austin, Texas. Association for Computational Linguistics. · DOI
  24. Walter Scheirer, Christopher Forstall, and Neil Coffee. 2016. The sense of a connection: Automatic tracing of intertextuality by meaning. Digital Scholarship in the Humanities, 31:204-217. · DOI
  25. Helmut Schmid. 1994. Probabilistic part-of-speech tag- ging using decision trees. In Proceedings of the International Conference on New Methods in Lan- guage Processing, Manchester.
  26. David A. Smith, Ryan Cordell, Elizabeth Maddock Dil- lon, Nick Stramp, and John Wilkerson. 2014. De- tecting and modeling local text reuse. In Proceed- ings of the 14th ACM/IEEE-CS Joint Conference on Digital Libraries, page 183-192. Institute of Electri- cal and Electronics Engineers. · DOI
  27. Rachele Sprugnoli, Marco Passarotti, Flavio Mas- similiano Cecchini, and Matteo Pellegrini. 2020. Overview of the EvaLatin 2020 evaluation cam- paign. In Proceedings of LT4HALA 2020 -1st Work- shop on Language Technologies for Historical and Ancient Languages, pages 105-110, Marseille. Eu- ropean Language Resources Association (ELRA).
  28. Rachele Sprugnoli, Marco Passarotti, and Giovanni Moretti. 2019. Vir is to moderatus as mulier is to intemperans: Lemma embeddings for Latin. In Pro- ceedings of the Sixth Italian Conference on Compu- tational Linguistics, Bari.
  29. Manuel Stoeckel, Alexander Henlein, Wahed Hemati, and Alexander Mehler. 2020. Voting for POS tag- ging of Latin texts: Using the flair of FLAIR to bet- ter ensemble classifiers by example of Latin. In Pro- ceedings of LT4HALA 2020 -1st Workshop on Lan- guage Technologies for Historical and Ancient Lan- guages, pages 130-135, Marseille. European Lan- guage Resources Association (ELRA).
  30. Milan Straka and Jana Straková. 2020. UDPipe at EvaLatin 2020: Contextualized embeddings and treebank embeddings. In Proceedings of LT4HALA 2020 -1st Workshop on Language Technologies for Historical and Ancient Languages, pages 124-129, Marseille. European Language Resources Associa- tion (ELRA).
  31. Richard F. Thomas. 2012. The streets of Rome: The classical Dylan. In William Brockliss, Pramit Chaudhuri, Ayelet Haimson Lushkov, and Katherine Wasdin, editors, Reception and the Classics: An In- terdisciplinary Approach to the Classical Tradition, pages 134-159. Cambridge University Press, Cam- bridge.
  32. Qi Zhang, Jihua Kang, Jin Qian, and Xuanjing Huang. 2014. Continuous word embeddings for detecting local text reuses at the semantic level. In Proceed- ings of the 37th International ACM SIGIR Confer- ence on Research & Development in Information Re- trieval, page 797-806, New York, New York. Asso- ciation for Computing Machinery. · DOI
  33. Andrew Zissos. 2008. Valerius Flaccus' Argonautica Book I, Edited with Introduction, Translation, and Commentary. Oxford University Press, Oxford.

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Metadata

Authors
Burns, P. J., Brofos, J. A., Li, K., Chaudhuri, P., & Dexter, J. P.
Affiliations
  • The University of Texas at Austin (US)
  • Yale University (US)
  • Columbia University (US)
  • Harvard University (US)
Corresponding author
Dexter, Joseph P.
ORCID
Venue
Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies
Publisher
Association for Computational Linguistics
Published
2021
Pages
4900-4907
DOI
10.18653/v1/2021.naacl-main.389
OpenAlex
W3171312764
Cited by
16
References
1
Citations by year
2026: 1 · 2025: 2 · 2024: 6 · 2023: 4 · 2022: 2 · 2021: 1
FWCI
49.25
Type
conference paper
Language
en
Open access
Licence
cc-by
Locations
Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (NAACL-HLT) (publishedVersion, cc-by)
Funders
  • National Science Foundation (1752134)
  • National Endowment for the Humanities
  • American Council of Learned Societies
Topics
  • Social Sciences › Arts and Humanities › Literature and Literary Theory › Digital Humanities and Scholarship
  • Social Sciences › Arts and Humanities › Language and Linguistics › Classical Studies and Philology
  • Physical Sciences › Computer Science › Artificial Intelligence › Topic Modeling
Keywords
intertextuality, Latin literature, word embeddings, classical texts

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