# A pattern recognition approach for distinguishing between prose and poetry

arXiv: https://arxiv.org/abs/2107.08512 · HTML: https://mlsanity.com/paper/2107.08512/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 6 of its 36 references that are in the map.
- First posted on arXiv 18 Jul 2021.

## Builds on (most cited first, 6 of 6)

- [Guess who? Multilingual approach for the automated generation of author-stylized poetry](https://mlsanity.com/paper/1807.07147/) (2018)
- [A systematic comparison of supervised classifiers](https://mlsanity.com/paper/1311.0202/) (2013)
- [Complex systems: features, similarity and connectivity](https://mlsanity.com/paper/1606.05400/) (2016)
- [Probing the topological properties of complex networks modeling short written texts](https://mlsanity.com/paper/1412.8504/) (2014)
- [Using network science and text analytics to produce surveys in a scientific topic](https://mlsanity.com/paper/1506.05690/) (2015)
- [A complex network approach to stylometry](https://mlsanity.com/paper/1506.09107/) (2015)

## Built on by (most cited first, 1 of 1)

- [Artificial Intelligence for Sentiment Analysis of Persian Poetry](https://mlsanity.com/paper/2603.11254/) (2026)

## Abstract (arXiv)

Poetry and prose are written artistic expressions that help us to appreciate the reality we live. Each of these styles has its own set of subjective properties, such as rhyme and rhythm, which are easily caught by a human reader's eye and ear. With the recent advances in artificial intelligence, the gap between humans and machines may have decreased, and today we observe algorithms mastering tasks that were once exclusively performed by humans. In this paper, we propose an automated method to distinguish between poetry and prose based solely on aural and rhythmic properties. In other to compare prose and poetry rhythms, we represent the rhymes and phones as temporal sequences and thus we propose a procedure for extracting rhythmic features from these sequences. The classification of the considered texts using the set of features extracted resulted in a best accuracy of 0.78, obtained with a neural network. Interestingly, by using an approach based on complex networks to visualize the similarities between the different texts considered, we found that the patterns of poetry vary much more than prose. Consequently, a much richer and complex set of rhythmic possibilities tends to be found in that modality.