Statistical Language Models

Statistical Language Models are probabilistic models that assign probabilities to sequences of words or tokens in a language, based on statistical analysis of text data. They are fundamental in natural language processing (NLP) for tasks like speech recognition, machine translation, and text generation, by predicting the likelihood of word sequences to understand and generate human language.

Also known as: SLMs, Statistical NLP Models, N-gram Models, Probabilistic Language Models, Statistical LMs

Why learn Statistical Language Models?

Developers should learn Statistical Language Models when working on NLP applications that require language understanding, prediction, or generation, such as chatbots, autocomplete features, or sentiment analysis. They are essential for building systems that process and produce human-like text, especially before the rise of deep learning models, and remain relevant for foundational NLP knowledge and lightweight applications.

Learning Resources

• book: Speech and Language Processing - Chapter 3: N-gram Language Models
• course: Statistical Language Models - Coursera Course
• tutorial: N-gram Language Models Tutorial by Stanford NLP
• docs: Statistical Language Modeling - Wikipedia
• video: Introduction to Statistical Language Models - YouTube Video