# Syllabic Quantity Patterns as Rhythmic Features for Latin Authorship Attribution

arXiv: https://arxiv.org/abs/2110.14203 · HTML: https://mlsanity.com/paper/2110.14203/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- Builds on 16 of its 86 references that are in the map.
- First posted on arXiv 27 Oct 2021.

## Builds on (most cited first, 16 of 16)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [PyTorch: An Imperative Style, High-Performance Deep Learning Library](https://mlsanity.com/paper/1912.01703/) (2019)
- [Character-level Convolutional Networks for Text Classification](https://mlsanity.com/paper/1509.01626/) (2015)
- [Recent Trends in Deep Learning Based Natural Language Processing](https://mlsanity.com/paper/1708.02709/) (2017)
- [Character-level and Multi-channel Convolutional Neural Networks for Large-scale Authorship Attribution](https://mlsanity.com/paper/1609.06686/) (2016)
- [The Importance of Suppressing Domain Style in Authorship Analysis](https://mlsanity.com/paper/2005.14714/) (2020)
- [Learning Stylometric Representations for Authorship Analysis](https://mlsanity.com/paper/1606.01219/) (2016)
- [Author Identification using Multi-headed Recurrent Neural Networks](https://mlsanity.com/paper/1506.04891/) (2015)
- [COCO_TS Dataset: Pixel-level Annotations Based on Weak Supervision for Scene Text Segmentation](https://mlsanity.com/paper/1904.00818/) (2019)
- [Siamese Networks for Large-Scale Author Identification](https://mlsanity.com/paper/1912.10616/) (2019)
- [Assessing the Applicability of Authorship Verification Methods](https://mlsanity.com/paper/1906.10551/) (2019)
- [Bleaching Text: Abstract Features for Cross-lingual Gender Prediction](https://mlsanity.com/paper/1805.03122/) (2018)
- [Relative contributions of Shakespeare and Fletcher in Henry VIII: An Analysis Based on Most Frequent Words and Most Frequent Rhythmic Patterns](https://mlsanity.com/paper/1911.05652/) (2019)
- [MedLatinEpi and MedLatinLit: Two Datasets for the Computational Authorship Analysis of Medieval Latin Texts](https://mlsanity.com/paper/2006.12289/) (2020)
- [Experiments with Neural Networks for Small and Large Scale Authorship Verification](https://mlsanity.com/paper/1803.06456/) (2018)

## Built on by (most cited first, 5 of 5)

- [Sui Generis: Large Language Models for Authorship Attribution and Verification in Latin](https://mlsanity.com/paper/2410.09245/) (2024)
- [Introducing Rhetorical Parallelism Detection: A New Task with Datasets, Metrics, and Baselines](https://mlsanity.com/paper/2312.00100/) (2023)
- [Same or Different? Diff-Vectors for Authorship Analysis](https://mlsanity.com/paper/2301.09862/) (2023)
- [Lyricist-Singer Entropy Affects Lyric-Lyricist Classification Performance](https://mlsanity.com/paper/2310.11035/) (2023)
- [Explainable Authorship Identification in Cultural Heritage Applications: Analysis of a New Perspective](https://mlsanity.com/paper/2311.02237/) (2023)

## Abstract (arXiv)

It is well known that, within the Latin production of written text, peculiar metric schemes were followed not only in poetic compositions, but also in many prose works. Such metric patterns were based on so-called syllabic quantity, i.e., on the length of the involved syllables, and there is substantial evidence suggesting that certain authors had a preference for certain metric patterns over others. In this research we investigate the possibility to employ syllabic quantity as a base for deriving rhythmic features for the task of computational authorship attribution of Latin prose texts. We test the impact of these features on the authorship attribution task when combined with other topic-agnostic features. Our experiments, carried out on three different datasets, using two different machine learning methods, show that rhythmic features based on syllabic quantity are beneficial in discriminating among Latin prose authors.