Words and Tokens: Language Models and Probabilities

This lecture reviews key concepts related to language models in natural language processing, focusing on the input of NLP systems, tokenization, and language identification. It also covers the estimation of probabilities using n-grams and techniques like Laplace smoothing and Good-Turing smoothing. The instructor emphasizes the importance of choosing the best sequence in language models and discusses the challenges of comparing probabilities in different spaces.