#GraphPoem: Automatic Classification of Rhyme and Diction in Poetry
(2021) Interférences littéraires — Vol. 25, n° 1, p. 218-235 (2021)
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- Abstract
- Our project—the Graph Poem (#GraphPoem)—focuses on poetry as a genre with the purpose to automate the reading of as many–if not, ideally, all–poetic features, and then quantify and deploy them in representing and analyzing poetic corpora as networks. The article argues for and illustrates the benefits–to both digital humanities (DH) and natural language processing (NLP)–of setting out to computationally analyze poetry comprehensively. While comprehensiveness can only be asymptotically pursued in such a complex genre, the specifics such pursuit involves (with its in-depth analysis of each particular poetic feature) branch out at times into other genres and more multiple and diverse text analysis aspects. The poetic features we focus on here are rhyme and diction, and metaphor, and our system establishes a computational model that classifies poems based on similarities. For rhyme analysis, we investigate the methods used to classify poems based on rhyme patterns and we achieve an accuracy of 96.51% in identifying rhymes in poetry by applying a phonetic similarity model. For diction analysis, we investigate the classification methods and also build a word embeddings model on our poetry dataset. The analysis of these classifiers occasions reflections on the complexity of computational poetry analysis and the prospects of our project at the crossroads of NLP, machine learning more generally, graph theory applications, and DH, as well as at the confluence of research and creative work.
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Tanasescu, C., & et al. (2021). #GraphPoem: Automatic Classification of Rhyme and Diction in Poetry. Interférences littéraires, 25(1), 218-235. https://hdl.handle.net/2078.5/111106 (Original work published 2021)