Neural networks have become increasingly popular for the task of language modeling. Whereas feed-forward networks only exploit a fixed context length to predict the next word of a sequence, conceptually, standard recurrent neural networks can take into account all of the predecessor words. In this paper, an algorithm using machine learning which, when given a dataset of conversations, is able to train itself using Neural Networks which can then be used to get suggestions for replies for any particular input sentence is proposed. Currently, smart suggestions have been implemented in chat applications. Google uses similar techniques to provide smart replies in Email through which the user can reply to a particular email with just a single tap. © 2018, Springer International Publishing AG.
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K. R. Bindu, Aakash, C., Orlando, B., and Parameswaran, L., “An algorithm for text prediction using neural networks”, Lecture Notes in Computational Vision and Biomechanics, vol. 28, pp. 186-192, 2018.