Recurrent Neural Network based Models for Word Prediction
S.Ramya1, C.S. Kanimozhi Selvi2
1Ms.S.Ramya, Computer Science and Engineering, Kongu Engineering College, Perunduari, Erode, Tamil Nadu, India.
2Dr.C.S.KanimozhiSelvi, Computer Science and Engineering, Kongu Engineering College, Perunduari, Erode, Tamil Nadu, India.
Manuscript received on November 20, 2019. | Revised Manuscript received on November 28, 2019. | Manuscript published on 30 November, 2019. | PP: 7433-7437 | Volume-8 Issue-4, November 2019. | Retrieval Number: D5313118419/2019©BEIESP | DOI: 10.35940/ijrte.D5313.118419
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC-BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: Globally, people are spending a cumulative amount of time on their mobile device, laptop, tab, desktop, etc,. for messaging, sending emails, banking, interaction through social media, and all other activities. It is necessary to cut down the time spend on typing through these devices. It can be achieved when the device can provide the user more options for what the next word might be for the current typed word. It also increases the speed of typing. In this paper, we suggest and presented a comparative study on various models like Recurrent Neural Network, Stacked Recurrent Neural Network, Long Short Term Memory network (LSTM) and Bi-directional LSTM that gives solution for the above said problem. Our primary goal is to suggest the best model among the four models to predict the next word for the given current word in English Language. Our survey says that for predicting next word RNN provide accuracy 60% and loss 40%, Stacked RNN provide accuracy 62% and loss 38%, LSTM provide accuracy 64% and loss 36% and Bidirectional LSTM provide accuracy 72% and loss 28%.
Keywords: Artificial Neural Networks, Recurrent Neural Networks, Long Short Term Memory, Bi-directional LSTM.
Scope of the Article: Artificial Intelligence and Machine Learning.