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

<span lang="EN-US">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 learn long-term dependencies. The present research integrates the LSTM dropout technique generate from corpus as input, model developed find best way extract words context. For training model, poem "</span><em><span lang="EN-US">La Ciudad y los perros</span></em><span lang="EN-US">" which composed 128,600 input data. was divided into two data sets, 38.88% for remaining 61.12% testing model. proposed tested variants: word importance results were evaluated terms semantic proximity generated given context.</span>