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        The Novel in the Spanish Silver Age

        A Digital Analysis of Genre Using Machine Learning

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        Author(s)
        Calvo Tello, José
        Language
        English
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        Abstract
        What distinguishes an adventure novel from a historical novel? Can the same text belong to several genres? More to one than to another? Have some existing genres been overlooked? To answer these and similar questions, José Calvo Tello combines methods from Linguistics (lexicography), Literary Studies (genre theory), and Computer Science (machine learning, natural language processing). Located in the interdisciplinary field of Digital Humanities, this study analyzes a newly developed corpus of 358 Spanish novels of the silver age (1880-1939), which includes authors like Baroja, Pardo Bazán, or Valle-Inclán. Calvo Tello's key result is a graph-based model of literary genre that reconciles recent theoretical approaches.
        URI
        https://library.oapen.org/handle/20.500.12657/51239
        Keywords
        Literature; Science; Spanish Literature; Digital Humanities; Theory of Literature; Romance Studies; Bielefeld University Press
        DOI
        10.1515/9783839459256
        ISBN
        9783839459256, 9783839459256, 9783837659252, 9783743559257
        Publisher
        Bielefeld University Press
        Publisher website
        https://www.bielefeld-university-press.org/
        Publication date and place
        2021
        Grantor
        • Bundesministerium für Forschung, Technologie und Raumfahrt - [grantnumber unknown]
        Imprint
        Bielefeld University Press
        Series
        Digital Humanities Research, 4
        Classification
        Literary studies: general
        Research methods: general
        Literary theory
        Pages
        470
        Rights
        https://creativecommons.org/licenses/by/4.0/
        • Imported or submitted locally

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        License

        • If not noted otherwise all contents are available under Attribution 4.0 International (CC BY 4.0)

        Credits

        • logo EU
        • This project received funding from the European Union's Horizon 2020 research and innovation programme under grant agreement No 683680, 810640, 871069 and 964352.

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