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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 many languages, numerals up to the base are a distinct part of speech, while the words for powers of the base belong to one of the other word classes. In English, these higher words are hundred 10 2 , thousand 10 3 , million 10 6 , and higher powers of a thousand ( short scale ) or of a million ( long scale —see names of large numbers ).
A sample test using an automated Gunning Fog calculator on a random footnote from the text (#51: Dion, vol. I. lxxix. p. 1363. Herodian, l. v. p. 189.) [9] gave an index of 19.2 using only the sentence count, and an index of 12.5 when including independent clauses. This brought down the fog index from post-graduate to high school level.
The concept of a "mass noun" is a grammatical concept and is not based on the innate nature of the object to which that noun refers. For example, "seven chairs" and "some furniture" could refer to exactly the same objects, with "seven chairs" referring to them as a collection of individual objects but with "some furniture" referring to them as a single undifferentiated unit.
Reed–Kellogg diagram of the sentence. The sentence is unpunctuated and uses three different readings of the word "buffalo". In order of their first use, these are: a. a city named Buffalo. This is used as a noun adjunct in the sentence; n. the noun buffalo, an animal, in the plural (equivalent to "buffaloes" or "buffalos"), in order to avoid ...
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 It has been superseded by recurrent neural network–based models, which have been superseded by large language models.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.
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]
When the items are words, n-grams may also be called shingles. [2] In the context of Natural language processing (NLP), the use of n-grams allows bag-of-words models to capture information such as word order, which would not be possible in the traditional bag of words setting.