Abstract
Following classical antiquity, European poetic meter was complicated by traditions negotiating between the prosodic stress of vernacular dialects and a classical system based on syllable length. Middle High German (MHG) epic poetry found a solution in a hybrid qualitative and quantitative meter. We develop a CRF model to predict the metrical values of syllables in MHG epic verse, achieving an Fscore of .894 on 10-fold cross-validated development data (outperforming several baselines) and .904 on held-out testing data. The method used in this paper presents itself as a viable option for other literary traditions, and as a tool for subsequent genre or author analysis.
Cite
CITATION STYLE
Estes, A., & Hench, C. (2016). Supervised machine learning for hybrid meter. In Proceedings of the 5th Workshop on Computational Linguistics for Literature, CLfL 2016 at the 2016 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, NAACL-HLT 2016 (pp. 1–8). Association for Computational Linguistics (ACL). https://doi.org/10.18653/v1/w16-0201
Register to see more suggestions
Mendeley helps you to discover research relevant for your work.
