Text prediction recurrent neural networks using long shortterm memory-dropout
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Fecha
2022-10-29Autor(es)
Iparraguirre-Villanueva, Orlando
Guevara-Ponce, Victor
Ruiz-Alvarado, Daniel
BeltozarClemente, Saul
Sierra-Liñan, Fernando
Zapata-Paulini, Joselyn
Cabanillas-Carbonell, Michael
Metadatos
Mostrar el registro completo del ítemResumen
“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.“
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