The Novel in the Spanish Silver Age : A Digital Analysis of Genre Using Machine Learning / José Calvo Tello

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 Studi...

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Superior document:Digital Humanities Research 4
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Year of Publication:2021
Edition:1st ed.
Language:English
Series:Digital humanities research ; 4
Physical Description:1 online resource (470 p.); 2144 MB 91 SW-Abbildungen, 32 Farbabbildungen
Notes:transcript Verlag
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520 |a 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. 
500 |a transcript Verlag 
545 0 |8 1\u  |a José Calvo Tello, born in 1987, works as a researcher and subject librarian at Göttingen State and University Library. He obtained his doctorate in Humanities from the Julius-Maximilians-Universität Würzburg (Germany) with a thesis about machine learning and other computational methods applied to the Spanish novel. His research is focused on the application and development of computational methods such as machine learning and natural language processing applied to romance literatures and library records. 
502 |b Doctoral Thesis  |c Universität Würzburg  |d 2020 
505 0 |a Frontmatter 1 Editorial 2 Contents 5 Acknowledgements 13 1. Introduction 19 2. Previous Research and Theoretical Framework 31 3. Data: Texts and Metadata 93 4. Feature Engineering: Linguistic Annotation and Transformation 177 5. Analysis of Subgenre Labels 221 6. Feature and Labels Selection 269 7. Analysis of Subgenres 321 8. Discussion of Tripartite Graph for Genre 367 9. Conclusion 405 10. References 423 11. Appendix 445 
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536 |a funded by Bundesministerium für Bildung und Forschung 
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