To understand and practice smoothing techniques for N-gram language models through interactive exploration of bigram probability estimation and handling of sparse data.

This experiment aims to help students develop proficiency in applying smoothing methods (such as Add-One Smoothing) to N-gram models, analyze the impact of smoothing on probability distributions, and understand how smoothing addresses the problem of zero-probability N-grams in natural language processing.