Next Word Prediction Model Using Long Short-Term Memory Networks and Machine Learning

Abstract- Next Word Prediction (NWP) is essential in applications like predictive text and virtual assistants. This study explores using Long Short-Term Memory (LSTM) network for NWP, addressing the limitations of traditional models such as n-grams in capturing long-term dependencies. The model is trained on preprocessed datasets, incorporating tokenization, sequence generation, and embedding layers to represent textual data. LSTM layers are utilized to understand sequential context, followed by dense layers for prediction. Performance is evaluated through metrics like perplexity and accuracy, with the LSTM model demonstrating superior contextual understanding and predictive accuracy compared to traditional methods. The research also suggests future improvements, including hyperparameter tuning and exploring transformer architectures, to enhance NWP performance across various applications.

Next-Word Prediction, LSTM, Natural Language Processing, Recurrent Neural Networks, Deep Learning

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