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

Research output: Contribution to journalArticlepeer-review

6 Scopus citations


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.

Original languageEnglish
Pages (from-to)1758-1768
Number of pages11
JournalIndonesian Journal of Electrical Engineering and Computer Science
Issue number3
StatePublished - Mar 2023
Externally publishedYes


  • Dropout
  • Prediction
  • Recurrent neural network
  • Text
  • Unit short-term memory


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