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If only one previous word is considered, it is called a bigram model; if two words, a trigram model; if n − 1 words, an n-gram model. [2] Special tokens are introduced to denote the start and end of a sentence s {\displaystyle \langle s\rangle } and / s {\displaystyle \langle /s\rangle } .
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The bag-of-words model (BoW) is a model of text which uses an unordered collection (a "bag") of words. It is used in natural language processing and information retrieval (IR). It disregards word order (and thus most of syntax or grammar) but captures multiplicity .
reStructuredText (RST, ReST, or reST) is a file format for textual data used primarily in the Python programming language community for technical documentation.. It is part of the Docutils project of the Python Doc-SIG (Documentation Special Interest Group), aimed at creating a set of tools for Python similar to Javadoc for Java or Plain Old Documentation (POD) for Perl.
Like the bag-of-words model, it models a document as a multiset of words, without word order. It is a refinement over the simple bag-of-words model, by allowing the weight of words to depend on the rest of the corpus. It was often used as a weighting factor in searches of information retrieval, text mining, and user modeling.
Wentian Li has shown that in a document in which each character has been chosen randomly from a uniform distribution of all letters (plus a space character), the "words" with different lengths follow the macro-trend of Zipf's law (the more probable words are the shortest and have equal probability). [19]
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]
The text string "comment" might be repeated in the label, the HTML tag, in a read function name, a private variable, database DDL, queries, and so on. A DRY approach eliminates that redundancy by using frameworks that reduce or eliminate all those editing tasks except the most important ones, leaving the extensibility of adding new knowledge ...