Supervised Machine Learning for Hybrid Meter

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.