A Preliminary Study for Literary Rhyme Generation based on Neuronal Representation, Semantics and Shallow Parsing

For many years, research in Artificial Intelligence (AI) has directed efforts towards automating processes to perform specific academic, industrial or economic tasks for society. However, the investigation and development of procedures for the automation of human artistic and creative processes has not had as much attention due to the complexities involved in these activities. Procedures developed for these purposes involve mathematical-computational methods designed to process and learn from a large quantity of digital data, so as to detect patterns in order to simulate the creative process (CP), as explained by Boden in [3]. In this paper, we introduce a model for the generation of rhymes with literary components. Our proposal is based on findings detailed in [11], where Automatic Text Generation (ATG) techniques are combined with neural network (NN) based models, such as the Word2vec algorithm [9], for the generation of literary texts.