Language models are designed to predict what words are likely to come next in a sequence, assigning probabilities to each possible continuation. In this article, we explore how an N-gram language model works, how it assigns probabilities to sentences and sequences of words, and then examine how well this model performs. By understanding and evaluating this approach, we gain insight into how language models handle the complexity of human language.
Applications
- Augmentative communication
- Machine translation
- Spelling correction
- Speech recognition
Language Models
Definition
A language model consists of a finite set V and a function f(x1, x2, …, xn) such that: