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  2. n-gram - Wikipedia

    en.wikipedia.org/wiki/N-gram

    An n-gram is a sequence of n adjacent symbols in particular order. [1] The symbols may be n adjacent letters (including punctuation marks and blanks), syllables , or rarely whole words found in a language dataset; or adjacent phonemes extracted from a speech-recording dataset, or adjacent base pairs extracted from a genome.

  3. Word n-gram language model - Wikipedia

    en.wikipedia.org/wiki/Word_n-gram_language_model

    Syntactic n-grams are intended to reflect syntactic structure more faithfully than linear n-grams, and have many of the same applications, especially as features in a vector space model. Syntactic n-grams for certain tasks gives better results than the use of standard n-grams, for example, for authorship attribution. [12]

  4. Katz's back-off model - Wikipedia

    en.wikipedia.org/wiki/Katz's_back-off_model

    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.

  5. w-shingling - Wikipedia

    en.wikipedia.org/wiki/W-shingling

    In natural language processing a w-shingling is a set of unique shingles (therefore n-grams) each of which is composed of contiguous subsequences of tokens within a document, which can then be used to ascertain the similarity between documents. The symbol w denotes the quantity of tokens in each shingle selected, or solved for.

  6. Kneser–Ney smoothing - Wikipedia

    en.wikipedia.org/wiki/Kneser–Ney_smoothing

    Kneser–Ney smoothing, also known as Kneser-Essen-Ney smoothing, is a method primarily used to calculate the probability distribution of n-grams in a document based on their histories. [1] It is widely considered the most effective method of smoothing due to its use of absolute discounting by subtracting a fixed value from the probability's ...

  7. Word2vec - Wikipedia

    en.wikipedia.org/wiki/Word2vec

    Embedding vectors created using the Word2vec algorithm have some advantages compared to earlier algorithms [1] such as those using n-grams and latent semantic analysis. GloVe was developed by a team at Stanford specifically as a competitor, and the original paper noted multiple improvements of GloVe over word2vec. [ 9 ]

  8. Which players are participating in the 2025 NFL combine? NFL ...

    www.aol.com/players-participating-2025-nfl...

    The NFL has announced its full list of 2025 NFL combine invitees. Here are all 329 NFL draft hopefuls that were invited to Indianapolis this year:

  9. BLEU - Wikipedia

    en.wikipedia.org/wiki/BLEU

    BLEU (bilingual evaluation understudy) is an algorithm for evaluating the quality of text which has been machine-translated from one natural language to another. Quality is considered to be the correspondence between a machine's output and that of a human: "the closer a machine translation is to a professional human translation, the better it is" – this is the central idea behind BLEU.