EXO Next Word Prediction Using Machine Learning

Abstract

It is never simple to write quickly without making mistakes. It's not difficult to type quickly and properly on a desktop computer, but many individuals find it challenging to type on compact devices like mobile phones. By simply guessing the word that will come next in a sentence, the next word prediction project makes it simpler for you to type on small devices. Due to the algorithm's ability to predict the following word and drastically reduce errors, you are not required to finish sentences. The creation of a language that is understandable by humans and sounds natural is the aim of natural language generation (NLG). This study suggests a novel technique for anticipating the following word in an English phrase. By assuming the next word in a series, the user can reduce the number of keystrokes they make. Two deep learning algorithms, Long Short Term Memory (LSTM) and Bi-LSTM were used to analyze the problem of predicting the next word. Accuracy values for LSTM and Bi-LSTM were 59.46% and 81.07%. Many NLG tasks, including sentence and narrative autocompletion, among others, can be accomplished using this technique.

They're still assuming the coming word grounded on the words that came ahead it’s essential to have clear-headed language models of the loftiest class, in fact, recent work on large-scale language models has shown that RNNs perform inadequately on their own but well when used in confluence with n-gram models this is because their advantages over n-gram models are fully different circles live in intermittent neural network networks they enable the preservation of information. Assists in reducing user keystrokes while typing.

• Assists users in saving typing time.


• Assists in reducing user spelling errors. Assists non-native speakers in learning the language by suggesting new and correct

Next Word Prediction, English, LSTM, and Bi- LSTM are some of the index terms.