A language model is constructed for each document in the collection.

It uses error-correction algorithms and a language model to guess the intended word.

The higher the quality the language model the better the experience of the user, especially in complex interactions.

A language model is a probability distribution over entire sentences or texts.

The final step uses the training corpus again for statistical tuning of the language model.

They also require a language model or grammar file.

Bigrams are used in one of the most successful language models for speech recognition.

With such large corpora it is believed that improved language models may be created.

Under this method, students listen to or view recordings of language models acting in situations.

This assumption is important because it massively simplifies the problem of learning the language model from data.