Examinando por Autor "BeltozarClemente, Saul"
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Publicación Acceso abierto Text prediction recurrent neural networks using long shortterm memory-dropout(Institute of Advanced Engineering and Science, 2022-10-29) Iparraguirre-Villanueva, Orlando; Guevara-Ponce, Victor; Ruiz-Alvarado, Daniel; BeltozarClemente, Saul; Sierra-Liñan, Fernando; Zapata-Paulini, Joselyn; Cabanillas-Carbonell, Michael“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.“