# A Comparison of Feature-Based and Neural Scansion of Poetry

arXiv: https://arxiv.org/abs/1711.00938 · HTML: https://mlsanity.com/paper/1711.00938/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 2 Nov 2017.

## Built on by (most cited first, 4 of 4)

- [Metrical Tagging in the Wild: Building and Annotating Poetry Corpora with Rhythmic Features](https://mlsanity.com/paper/2102.08858/) (2021)
- [ALBERTI, a Multilingual Domain Specific Language Model for Poetry Analysis](https://mlsanity.com/paper/2307.01387/) (2023)
- [MetricalARGS: A Taxonomy for Studying Metrical Poetry with LLMs](https://mlsanity.com/paper/2510.08188/) (2025)
- [PARSI: Persian Authorship Recognition via Stylometric Integration](https://mlsanity.com/paper/2506.21840/) (2025)

## Abstract (arXiv)

Automatic analysis of poetic rhythm is a challenging task that involves linguistics, literature, and computer science. When the language to be analyzed is known, rule-based systems or data-driven methods can be used. In this paper, we analyze poetic rhythm in English and Spanish. We show that the representations of data learned from character-based neural models are more informative than the ones from hand-crafted features, and that a Bi-LSTM+CRF-model produces state-of-the art accuracy on scansion of poetry in two languages. Results also show that the information about whole word structure, and not just independent syllables, is highly informative for performing scansion.