Neural Network-Based Linguistic Framework for Successive Word Prediction to Enhance Language Processing

The next word prediction process is crucial for improving natural language processing applications and aiding in the creation of coherent and contextually appropriate texts. This project focuses on the use of Long Short-Term Memory (LSTM) networks for next word prediction tasks, which are a specific type of recurrent neural network (RNN). Utilizing the strengths of LSTM, the research aims to develop a system that can accurately predict the following word in a text sequence. The study demonstrates the effectiveness and accuracy of LSTM by examining its methodological framework, architectural structure, training techniques, and performance evaluation metrics.