Text prediction recurrent neural networks using long short-term memory-dropout

Orlando Iparraguirre-Villanueva, Victor Guevara-Ponce, Daniel Ruiz-Alvarado, Saul Beltozar-Clemente, Fernando Sierra-Liñan, Joselyn Zapata-Paulini, Michael Cabanillas-Carbonell

Resultado de la investigación: Artículo CientíficoArtículo originalrevisión exhaustiva

2 Citas (Scopus)

Resumen

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.

Idioma originalInglés estadounidense
Páginas (desde-hasta)1758-1768
-11
PublicaciónIndonesian Journal of Electrical Engineering and Computer Science
Volumen29
N.º3
DOI
EstadoIndizado - mar. 2023

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