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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 It has been superseded by recurrent neural network –based models, which have been superseded by large language models.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.
Culturomics. Not to be confused with Culturomics (microbiology). Culturomics is a form of computational lexicology that studies human behavior and cultural trends through the quantitative analysis of digitized texts. [1][2] Researchers data mine large digital archives to investigate cultural phenomena reflected in language and word usage. [3]
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.
Smoothing is a conceptual bridge between zeta function regularization, with its reliance on complex analysis, and Ramanujan summation, with its shortcut to the Euler–Maclaurin formula. Instead, the method operates directly on conservative transformations of the series, using methods from real analysis .
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 ...