Extracting -gram statistics is a prevalent approach for statistical language modeling.
Statistical language models can be estimated based on various approaches.
The "R" statistical language has been growing growing in popularity as support increases in the open-source and academic communities.
General references for statistical language processing are Manning and Schütze 1999 and Jurafsky and Martin 2009.
Once the text has been processed, a bigram statistical language model is automatically created.
Statistical analysis of the phonemic corpus enables to develop statistical language models, based on phonemes.
In statistical language modelling, data sparseness is particularly acute in the case of morphologically rich languages.
Statistical language modelling enables to develop large vocabulary and effective speech recognition systems [5].
One use of my assessments is to reveal whether students are using statistical language properly.
Such a method is known as data smoothing in statistical language processing.
Statistical language models estimate the probability for a given sequence of words.
