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


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.


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

Full Text:


DOI: http://doi.org/10.11591/ijeecs.v29.i3.pp1758-1768


  • There are currently no refbacks.

Creative Commons License
This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.

Indonesian Journal of Electrical Engineering and Computer Science (IJEECS)
p-ISSN: 2502-4752, e-ISSN: 2502-4760
This journal is published by the Institute of Advanced Engineering and Science (IAES) in collaboration with Intelektual Pustaka Media Utama (IPMU).

shopify stats IJEECS visitor statistics