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The program can search for a word or a phrase, including misspellings or gibberish. [5] The n-grams are matched with the text within the selected corpus, and if found in 40 or more books, are then displayed as a graph. [6] The Google Books Ngram Viewer supports searches for parts of speech and wildcards. [6] It is routinely used in research. [7 ...
Word frequency is known to have various effects (Brysbaert et al. 2011; Rudell 1993). Memorization is positively affected by higher word frequency, likely because the learner is subject to more exposures (Laufer 1997). Lexical access is positively influenced by high word frequency, a phenomenon called word frequency effect (Segui et al.).
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 It has been superseded by recurrent neural network–based models, which have been superseded by large language models.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.
Word count is commonly used by translators to determine the price of a translation job. Word counts may also be used to calculate measures of readability and to measure typing and reading speeds (usually in words per minute). When converting character counts to words, a measure of 5 or 6 characters to a word is generally used for English. [1]
Frequency analysis, the study of the frequency of letters or groups of letters; Letter frequencies; Oxford English Corpus; Swadesh list, a compilation of basic concepts for the purpose of historical-comparative linguistics; Zipf's law, a theory stating that the frequency of any word is inversely proportional to its rank in a frequency table
A word is considered to be high frequency if the word is commonly used in daily speech, such as the word "the". A word is considered to be low frequency if the word is not commonly used, such as the word "strait". [4] Some languages such as Chinese have multiple levels of daily speech that impact frequency of words.
It disregards word order (and thus most of syntax or grammar) but captures multiplicity. The bag-of-words model is commonly used in methods of document classification where, for example, the (frequency of) occurrence of each word is used as a feature for training a classifier. [1] It has also been used for computer vision. [2]
The Academic Word List (AWL) is a word list of 570 English word families [1] which appear with great frequency in a broad range of academic texts. The target readership is English as a second or foreign language students intending to enter English-medium higher education , and teachers of such students.
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