A pattern recognition approach for distinguishing between prose and poetry
This work addresses a domain-specific task in computational linguistics for text analysis, but it is incremental as it applies existing methods to a new dataset.
The authors tackled the problem of automatically distinguishing poetry from prose using aural and rhythmic features, achieving a best accuracy of 0.78 with a neural network classifier.
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.