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  2. Word n-gram language model - Wikipedia

    en.wikipedia.org/wiki/Word_n-gram_language_model

    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 − 1 words, an n-gram model. [2]

  3. n-gram - Wikipedia

    en.wikipedia.org/wiki/N-gram

    Figure 1 n-gram examples from various disciplines ; Field Unit Sample sequence 1-gram sequence 2-gram sequence 3-gram sequence Vernacular name unigram bigram trigram Order of resulting Markov model

  4. Trigram tagger - Wikipedia

    en.wikipedia.org/wiki/Trigram_tagger

    It is trained on a text corpus as a method to predict the next word, taking the product of the probabilities of unigram, bigram and trigram. In speech recognition, algorithms utilizing trigram-tagger score better than those algorithms utilizing IIMM tagger but less well than Net tagger.

  5. Letter frequency - Wikipedia

    en.wikipedia.org/wiki/Letter_frequency

    Letter, bigram, trigram, word frequencies, word length, and sentence length can be calculated for specific authors and used to prove or disprove authorship of texts, even for authors whose styles are not so divergent. Accurate average letter frequencies can only be gleaned by analyzing a large amount of representative text.

  6. Bigram - Wikipedia

    en.wikipedia.org/wiki/Bigram

    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.

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  8. Kneser–Ney smoothing - Wikipedia

    en.wikipedia.org/wiki/Kneser–Ney_smoothing

    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.

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