A Hybrid Approach to Stanza Classification in Spanish Poetry

The creation and analysis of poetry have been commonly carried out by hand; with only a few computer-assisted approaches appearing over the years. In the Spanish context, the promise of machine learning is starting to pan out in specific tasks such as metrical annotation and rhythm extraction. Among the possible tasks that comprise the analysis of a poem, identifying the type of a stanza remains underexplored. The classification of the inner structures of verses in which a poem is built upon is an especially relevant task for poetry studies since it complements the structural information of a poem. In this work, we analyzed different computational approaches to stanza classification in the Spanish poetic tradition. We collected a corpus of 5005 stanzas of 46 different types, and created a baseline expert system on a set of rules defined by poetry scholars. We show that this task continues to be hard for computers systems even when leveraging the best performing embeddings. However, combining the knowledge of experts as prior to machine learning approaches yields rates of accuracy around 92%. We believe that this combination of approaches could improve many other tasks, as the rules that govern poetry are somewhat arbitrary and hard for computers to learn from examples.