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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 ...
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 ...
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 It has been superseded by recurrent neural network –based models, which have been superseded by large language models.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.
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Smoothing. In statistics and image processing, to smooth a data set is to create an approximating function that attempts to capture important patterns in the data, while leaving out noise or other fine-scale structures/rapid phenomena. In smoothing, the data points of a signal are modified so individual points higher than the adjacent points ...
Document-term matrix. A document-term matrix is a mathematical matrix that describes the frequency of terms that occur in each document in a collection. In a document-term matrix, rows correspond to documents in the collection and columns correspond to terms. This matrix is a specific instance of a document-feature matrix where "features" may ...
The factored language model ( FLM) is an extension of a conventional language model introduced by Jeff Bilmes and Katrin Kirchoff in 2003. In an FLM, each word is viewed as a vector of k factors: An FLM provides the probabilistic model where the prediction of a factor is based on parents . For example, if represents a word token and represents ...