Computational Stylistics in Poetry, Prose, and Drama

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 NLP, including deep learning. 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.