Word n-gram language model: Difference between revisions

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A '''word ''n''-gram language model''' is a purely statistical model of language. It has been superseded by [[recurrent neural network]]–based models, which have been superseded by [[large language model]]s.<ref>{{Cite journal |url=https://dl.acm.org/doi/10.5555/944919.944966 |title=A neural probabilistic language model |first1=Yoshua |last1=Bengio |first2=Réjean |last2=Ducharme |first3=Pascal |last3=Vincent |first4=Christian |last4=Janvin |date=March 1, 2003 |journal=The Journal of Machine Learning Research |volume=3 |pages=1137–1155 |via=ACM Digital Library}}</ref> It is based on an assumption that the probability of the next word in a sequence depends only on a fixed size window of previous words. If only one previous word is considered, it is called a bigram model; if two words, a trigram model; if ''n''&nbsp;−&nbsp;1 words, an ''n''-gram model.<ref name=jm/> Special tokens are introduced to denote the start and end of a sentence <math>\langle s\rangle</math> and <math>\langle /s\rangle</math>.

To prevent a zero probability being assigned to unseen words, each word's probability is slightly higher than its frequency count in a [[Text corpus|corpus]]. To calculate it, various methods were used, from simple "add-one" smoothing (assign a count of 1 to unseen ''n''-grams, as an [[uninformative prior]]) to more sophisticated models, such as [[Good–Turing discounting]] or [[Katz's back-off model|back-off models]].

== Unigram model ==