Next Word Prediction System using NLP

This research paper introduces a novel statistical trigram language model designed for text prediction tasks, implemented using the Natural Language Toolkit (NLTK). The model is trained on the renowned “Alice in Wonderland” corpus, leveraging a weighted random probability approach to predict the subsequent word in a provided phrase. A distinctive feature of this model is its user-controlled prediction termination, allowing individuals to determine when the prediction process should halt. To ensure data quality, the text undergoes rigorous preprocessing stages, eliminating superfluous characters and ensuring standardized tokenization into trigrams through NLTK’s robust capabilities. The research not only presents the predictive model but also includes comprehensive n-gram statistics, offering insights into the underlying linguistic structures. Additionally, a visually informative frequency distribution plot showcasing the top 30 most common trigrams enriches the analysis, aiding in understanding the corpus’s textual patterns. This paper contributes to advancing text prediction methodologies by combining statistical modeling, NLTK proficiency, and user interactivity, offering a valuable tool for language enthusiasts, researchers, and developers alike.