Text prediction recurrent neural networks using long shortterm memory-dropout

• Autores: Iparraguirre-Villanueva, Orlando, Guevara-Ponce, Victor, Ruiz-Alvarado, Daniel, Beltozar-Clemente, Saul, Sierra-Liñan, Fernando, Zapata-Paulini, Joselyn, Cabanillas-Carbonell, Michael
• Formato: artículo
• Fecha de Publicación: 2023
• Institución: Universidad Autónoma del Perú
• Repositorio: AUTONOMA-Institucional
• Lenguaje: inglés
• OAI Identifier: oai:repositorio.autonoma.edu.pe:20.500.13067/2830
• Enlace del recurso: https://hdl.handle.net/20.500.13067/2830; https://doi.org/10.11591/ijeecs.v29.i3.pp1758-1768
• Nivel de acceso: acceso abierto
• Materia: Dropout; Prediction; Recurrent neural network; Text; Unit short-term memory; https://purl.org/pe-repo/ocde/ford#2.02.04

Unit short-term memory (LSTM) is a type of recurrent neural network (RNN) whose sequence-based models are being used in text generation and/or prediction tasks, question answering, and classification systems due to their ability to learn long-term dependencies. The present research integrates the LSTM network and dropout technique to generate a text from a corpus as input, a model is developed to find the best way to extract the words from the context. For training the model, the poem "La Ciudad y los perros" which is composed of 128,600 words is used as input data. The poem was divided into two data sets, 38.88% for training and the remaining 61.12% for testing the model. The proposed model was tested in two variants: word importance and context. The results were evaluated in terms of the semantic proximity of the generated text to the given context.