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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]
In the graph below, which deals with the English Wikipedia, the number of articles (blue, multiplied by 1,000) and revisions (yellow, right hand scale) is increasing very much faster than is the number of words per article (purple). This graph is based on November 7th 2005 figures from Wikistats. For explanation and technical analysis, see foot ...
The Coleman–Liau index is a readability test designed by Meri Coleman and T. L. Liau to gauge the understandability of a text. Like the Flesch–Kincaid Grade Level, Gunning fog index, SMOG index, and Automated Readability Index, its output approximates the U.S. grade level thought necessary to comprehend the text.
Each volume is assumed to be 25 cm (9.8 in) tall, 5 cm (2.0 in) thick, and containing 1,600,000 words or 8,000,000 characters. The size of this illustration is based upon the live article count manually adjusted by the average word count on an irregular basis on a user subpage.
The following table shows counts of pairs of words getting the most and the least PMI scores in the first 50 millions of words in Wikipedia (dump of October 2015) [citation needed] filtering by 1,000 or more co-occurrences. The frequency of each count can be obtained by dividing its value by 50,000,952.
This wiki template is to ease the use of text counting within Word Association Game. {{Wikipedia:Department of Fun/Word Count}} produces the following text: Word count is / as of word: . The parameters must be set, otherwise it produces a dull text.
The dictionary contains 157,000 combinations and derivatives, and 169,000 phrases and combinations, making a total of over 600,000 word-forms. [40] [41] There is one count that puts the English vocabulary at about 1 million words—but that count presumably includes words such as Latin species names, prefixed and suffixed words, scientific ...
which shows which documents contain which terms and how many times they appear. Note that, unlike representing a document as just a token-count list, the document-term matrix includes all terms in the corpus (i.e. the corpus vocabulary), which is why there are zero-counts for terms in the corpus which do not also occur in a specific document.