Next Word Prediction Using ML And DA

Abstract

Next-word prediction is a natural language processing task that involves predicting the most likely next word given a sequence of words. It is a fundamental problem in many applications, such as speech recognition, machine translation, and text generation. The main research problem for building a next-word prediction system is to develop an accurate and efficient model that can predict the next word in a given sequence of words. The model needs to be able to handle the complexity of natural language, including the variability in word order, syntax, and semantics. It should also be able to learn from a large corpus of text data and generalize well to new data. There are several challenges associated with building a next-word prediction system, including dealing with out-of-vocabulary words, handling long-term dependencies, and avoiding overfitting. These challenges require careful design choices, such as the choice of neural network architecture, training data, and hyperparameters. Overall, the research problem for building a next-word prediction system is to develop a model that can accurately predict the next word in a sequence of words, while also being robust to the complexities and variability of natural language. This is a challenging problem that requires expertise in natural language processing, machine learning, and deep learning, and has many potential applications in fields such as artificial intelligence, speech recognition, and text generation.