Quantitative criticism of literary relationships
Name
Dexter-2017-Quantitative criticism of literary.pdf
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804.59 KB
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Author(s)
Dexter, Joseph P.
Tripuraneni, Nilesh
Dasgupta, Tathagata
Kannan, Ajay
Brofos, James A.
Bonilla Lopez, Jorge A.
Schroeder, Lea A.
Casarez, Adriana
Rabinovich, Maxim
Haimson Lushkov, Ayelet
Date Issued
February 2017
Journal
Proceedings of the National Academy of Sciences
Publisher
National Academy of Sciences
Citation
Dexter, Joseph P., et al. “Quantitative Criticism of Literary Relationships.” Proceedings of the National Academy of Sciences, vol. 114, no. 16, Apr. 2017, pp. E3195–204. © 2017 National Academy of Sciences
Version
Final published version
Abstract
Authors often convey meaning by referring to or imitating prior works of literature, a process that creates complex networks of literary relationships ("intertextuality") and contributes to cultural evolution. In this paper, we use techniques from stylometry and machine learning to address subjective literary critical questions about Latin literature, a corpus marked by an extraordinary concentration of intertextuality. Our work, which we term "quantitative criticism," focuses on case studies involving two influential Roman authors, the playwright Seneca and the historian Livy. We find that four plays related to but distinct from Seneca's main writings are differentiated from the rest of the corpus by subtle but important stylistic features. We offer literary interpretations of the significance of these anomalies, providing quantitative data in support of hypotheses about the use of unusual formal features and the interplay between sound and meaning. The second part of the paper describes a machine-learning approach to the identification and analysis of citational material that Livy loosely appropriated from earlier sources. We extend our approach to map the stylistic topography of Latin prose, identifying the writings of Caesar and his near-contemporary Livy as an inflection point in the development of Latin prose style. In total, our results reflect the integration of computational and humanistic methods to investigate a diverse range of literary questions.
MIT Department
Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science
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Article is made available in accordance with the publisher's policy and may be subject to US copyright law. Please refer to the publisher's site for terms of use.
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DOI of Published Version
https://doi.org/10.1073/pnas.1611910114
