Next Word Prediction System Using NLP

This study focuses on Natural Language Processing (NLP) by developing a Next Word Prediction system utilizing Long Short-Term Memory (LSTM) networks. The system is built to take advantage of LSTM's capacity to capture temporal dependencies in text data using TensorFlow's Keras package. The major objective is to enhance content development and writing aid by precisely predicting the following word in a series. This will facilitate various apps, including virtual assistants., and increase typing speed. The first portion of the paper examines several NLP techniques., with a focus on Recurrent Neural Networks (RNNs) and some of their variants., including LSTM. The effectiveness of different architectural configurations and training methods is investigated through comprehensive testing and review to maximize predicted accuracy. The literature review sheds light on current methods., such as hybrid models., inflected language processing strategies., and deep learning architectures like Transformers. The methodology is informed by these sources and consists of preprocessing the data., modeling with Sequential LSTM architecture, and carrying out a comprehensive evaluation with metrics such as top-k accuracy and perplexity. Additional validation for the suggested approach comes from comparative evaluations using ablation experiments and baseline models. The results show that the LSTM-based model's high accuracy over a variety of datasets indicates its capacity to understand intricate linguistic patterns. Analyzing intricate systems., adding context., and modifying models for certain domains are a few possible areas for development. The project's end highlights the importance of LSTM networks in language modeling