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The Google Books Ngram Viewer is an online search engine that charts the frequencies of any set of search strings using a yearly count of n -grams found in printed sources published between 1500 and 2022 [1][2][3][4] in Google 's text corpora in English, Chinese (simplified), French, German, Hebrew, Italian, Russian, or Spanish. [1][2][5] There ...
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 It has been superseded by recurrent neural network –based models, which have been superseded by large language models.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.
n. -gram. An n-gram is a sequence of n adjacent symbols in particular order. 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.
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 ...
Katz back-off is a generative n -gram language model that estimates the conditional probability of a word given its history in the n -gram. It accomplishes this estimation by backing off through progressively shorter history models under certain conditions. [1] By doing so, the model with the most reliable information about a given history is ...
A language model is a probabilistic model of a natural language. [1] In 1980, the first significant statistical language model was proposed, and during the decade IBM performed ‘Shannon-style’ experiments, in which potential sources for language modeling improvement were identified by observing and analyzing the performance of human subjects in predicting or correcting text.
Good suggestion. Most of mentions to natural language applications and smoothing techniques in this article should be moved to an independent article about n-gram language models. A (hopefully, high-level) summary of the definition of n-gram language models and applications would be nice to have here, though.
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