A computational stylistics of poetry : distant reading and modeling of German and English verse

Abstract

This doctoral thesis is about the computational modeling of stylistic variation in poetry. As ‘a computational stylistics’ it examines the forms, social embedding, and the aesthetic potential of literary texts by means of computational and statistical methods, ranging from simple counting over information theoretic measures to neural network models, including experiments with representation learning, transfer learning, and multi-task learning. We built small corpora to manually annotate a number of phenomena that are relevant for poetry, such as meter, rhythm, rhyme, and also emotions and aesthetic judgements that are elicited in the reader. A strict annotation workflow allows us to better understand these phenomena, from how to conceptualize them and which problems arise when trying to annotate them on a larger scale. Furthermore, we built large corpora to discover patterns in a wide historical, aesthetic and linguistic range, with a focus on German and English writing, encompassing public domain texts from the late 16th century up into the early 20th century. These corpora are published with metadata and reliable automatic annotation of part-of-speech tags, syllable boundaries, meter and verse measures. This thesis contains chapters on diachronic variation, aesthetic emotions, and modeling prosody, including experiments that also investigate the interaction between them. We look at how the diction of poets in different languages changed over time, which topics and metaphors were and became popular, both as a reaction to aesthetic considerations and also the political climate of the time. We investigate which emotions are elicited in readers when they read poetry, how that relates to aesthetic judgements, how we can annotate such emotions, and then train models to learn them. Also, we present experiments on how to annotate prosodic devices on a large scale, how well we can train computational models to predict the prosody from text, and how informative those devices are for each other.