Computational Stylistics in Poetry, Prose, and Drama
Компьютерная стилистика в поэзии, прозе и драматургии
2022-11-23
SCID: 54.1/97yy76ua
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computational stylisticsmachine learningmotif analysisnatural language processingnetwork analysis
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Abstract (AI)
This volume responds to the current interest in computational and statistical methods to describe and analyse metre, style, and poeticity, particularly insofar as they can open up new research perspectives in literature, linguistics, and literary history. The contributions are representative of the diversity of approaches, methods, and goals of a thriving research community. Although most papers focus on written poetry, including computer-generated poetry, the volume also features analyses of spoken poetry, narrative prose, and drama. The contributions employ a variety of methods and techniques ranging from motif analysis, network analysis, machine learning, and Natural Language Processing. The volume pays particular attention to annotation, one of the most basic practices in computational stylistics. This contribution to the growing, dynamic field of digital literary studies will be useful to both students and scholars looking for an overview of current trends, relevant methods, and possible results, at a crucial moment in the development of novel approaches, when one needs to keep in mind the qualitative, hermeneutical benefit made possible by such quantitative efforts.
Key Findings
1
Annotation receives particular emphasis as a foundational practice in computational stylistics.
2
Its contributions cover written and spoken poetry, computer-generated poetry, narrative prose, and drama, reflecting broad applicability across genres.
3
The collection argues that quantitative literary analysis should retain its qualitative and hermeneutic value within digital literary studies.
4
The volume demonstrates that computational and statistical methods can generate new perspectives on metre, style, and poeticity in literary research.
5
The volume showcases diverse techniques, including motif analysis, network analysis, machine learning, and natural language processing.
Research Object
Literary texts, especially written poetry, as well as spoken poetry, narrative prose, and drama
Research Subject
Computational and statistical characterization of metre, style, and poeticity, including annotation and related literary-analytic patterns
Publication Details
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2022-11-23
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