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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 It has been superseded by recurrent neural network–based models, which have been superseded by large language models. [1] 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.
Figure 1 shows several example sequences and the corresponding 1-gram, 2-gram and 3-gram sequences. Here are further examples; these are word-level 3-grams and 4-grams (and counts of the number of times they appeared) from the Google n-gram corpus. [4] 3-grams ceramics collectables collectibles (55) ceramics collectables fine (130)
A language model is a model of natural language. [1] Language models are useful for a variety of tasks, including speech recognition, [2] machine translation, [3] natural language generation (generating more human-like text), optical character recognition, route optimization, [4] handwriting recognition, [5] grammar induction, [6] and information retrieval.
The query likelihood model is a language model [1] [2] used in information retrieval. A language model is constructed for each document in the collection. It is then possible to rank each document by the probability of specific documents given a query. This is interpreted as being the likelihood of a document being relevant given a query.
A bigram or digram is a sequence of two adjacent elements from a string of tokens, which are typically letters, syllables, or words.A bigram is an n-gram for n=2.. The frequency distribution of every bigram in a string is commonly used for simple statistical analysis of text in many applications, including in computational linguistics, cryptography, and speech recognition.
If it appears several times in a training corpus, the frequency of the unigram "Francisco" will also be high. Relying on only the unigram frequency to predict the frequencies of n-grams leads to skewed results; [3] however, Kneser–Ney smoothing corrects this by considering the frequency of the unigram in relation to possible words preceding it.
Charging your phone battery to 100% consistently can damage the life of the battery over the long term. Klaus Vedfelt via Getty Images
The equation for Katz's back-off model is: [2] (+) = {+ (+) (+) (+) > + (+)where C(x) = number of times x appears in training w i = ith word in the given context. Essentially, this means that if the n-gram has been seen more than k times in training, the conditional probability of a word given its history is proportional to the maximum likelihood estimate of that n-gram.