Quantitative criticism of literary relationships
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 subjectiv...
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Published in | Proceedings of the National Academy of Sciences - PNAS Vol. 114; no. 16; pp. E3195 - E3204 |
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Main Authors | , , , , , , , , , , , |
Format | Journal Article |
Language | English |
Published |
United States
National Academy of Sciences
18.04.2017
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Series | PNAS Plus |
Subjects | |
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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. |
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AbstractList | 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. Significance Famous works of literature can serve as cultural touchstones, inviting creative adaptations in subsequent writing. To understand a poem, play, or novel, critics often catalog and analyze these intertextual relationships. The study of such relationships is challenging because intertextuality can take many forms, from direct quotation to literary imitation. Here, we show that techniques from authorship attribution studies, including stylometry and machine learning, can shed light on inexact literary relationships involving little explicit text reuse. We trace the evolution of features not tied to individual words across diverse corpora and provide statistical evidence to support interpretive hypotheses of literary critical interest. The significance of this approach is the integration of quantitative and humanistic methods to address aspects of cultural evolution. 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. Famous works of literature can serve as cultural touchstones, inviting creative adaptations in subsequent writing. To understand a poem, play, or novel, critics often catalog and analyze these intertextual relationships. The study of such relationships is challenging because intertextuality can take many forms, from direct quotation to literary imitation. Here, we show that techniques from authorship attribution studies, including stylometry and machine learning, can shed light on inexact literary relationships involving little explicit text reuse. We trace the evolution of features not tied to individual words across diverse corpora and provide statistical evidence to support interpretive hypotheses of literary critical interest. The significance of this approach is the integration of quantitative and humanistic methods to address aspects of cultural evolution. 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. |
Author | Dasgupta, Tathagata Schroeder, Lea A. Chaudhuri, Pramit Katz, Theodore Tripuraneni, Nilesh Dexter, Joseph P. Kannan, Ajay Lushkov, Ayelet Haimson Casarez, Adriana Lopez, Jorge A. Bonilla Rabinovich, Maxim Brofos, James A. |
Author_xml | – sequence: 1 givenname: Joseph P. surname: Dexter fullname: Dexter, Joseph P. organization: Department of Systems Biology, Harvard Medical School, Boston, MA 02115 – sequence: 2 givenname: Theodore surname: Katz fullname: Katz, Theodore organization: The Dalton School, New York, NY 10128 – sequence: 3 givenname: Nilesh surname: Tripuraneni fullname: Tripuraneni, Nilesh organization: Department of Engineering, University of Cambridge, Cambridge CB2 1PZ, United Kingdom – sequence: 4 givenname: Tathagata surname: Dasgupta fullname: Dasgupta, Tathagata organization: Department of Systems Biology, Harvard Medical School, Boston, MA 02115 – sequence: 5 givenname: Ajay surname: Kannan fullname: Kannan, Ajay organization: Department of Computer Science, Dartmouth College, Hanover, NH 03755 – sequence: 6 givenname: James A. surname: Brofos fullname: Brofos, James A. organization: Department of Computer Science, Dartmouth College, Hanover, NH 03755 – sequence: 7 givenname: Jorge A. Bonilla surname: Lopez fullname: Lopez, Jorge A. Bonilla organization: Department of Classics, Dartmouth College, Hanover, NH 03755 – sequence: 8 givenname: Lea A. surname: Schroeder fullname: Schroeder, Lea A. organization: Department of Classics, Dartmouth College, Hanover, NH 03755 – sequence: 9 givenname: Adriana surname: Casarez fullname: Casarez, Adriana organization: Austin Independent School District, Austin, TX 78703 – sequence: 10 givenname: Maxim surname: Rabinovich fullname: Rabinovich, Maxim organization: Department of Electrical Engineering and Computer Science, University of California, Berkeley, CA 94720 – sequence: 11 givenname: Ayelet Haimson surname: Lushkov fullname: Lushkov, Ayelet Haimson organization: Department of Electrical Engineering and Computer Science, University of California, Berkeley, CA 94720 – sequence: 12 givenname: Pramit surname: Chaudhuri fullname: Chaudhuri, Pramit organization: Department of Classics, University of Texas, Austin, TX 78712 |
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Copyright | Volumes 1–89 and 106–114, copyright as a collective work only; author(s) retains copyright to individual articles Copyright National Academy of Sciences Apr 18, 2017 |
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Keywords | stylometry intertextuality authorship attribution machine learning cultural evolution |
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Notes | ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 23 Author contributions: J.P.D., T.K., N.T., T.D., M.R., A.H.L., and P.C. designed research; J.P.D., T.K., N.T., A.K., J.A.B., J.A.B.L., L.A.S., A.C., and P.C. performed research; J.P.D., T.K., N.T., T.D., A.K., J.A.B., J.A.B.L., L.A.S., A.C., A.H.L., and P.C. analyzed data; and J.P.D., A.H.L., and P.C. wrote the paper. 1J.P.D., T.K., N.T., and T.D. contributed equally to this work. Edited by Kenneth W. Wachter, University of California, Berkeley, CA, and approved February 27, 2017 (received for review July 20, 2016) |
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Snippet | Authors often convey meaning by referring to or imitating prior works of literature, a process that creates complex networks of literary relationships... Significance Famous works of literature can serve as cultural touchstones, inviting creative adaptations in subsequent writing. To understand a poem, play, or... Famous works of literature can serve as cultural touchstones, inviting creative adaptations in subsequent writing. To understand a poem, play, or novel,... |
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Title | Quantitative criticism of literary relationships |
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