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In computer science, an algorithm for matching wildcards (also known as globbing) is useful in comparing text strings that may contain wildcard syntax. [1] Common uses of these algorithms include command-line interfaces, e.g. the Bourne shell [2] or Microsoft Windows command-line [3] or text editor or file manager, as well as the interfaces for some search engines [4] and databases. [5]
In computer science, the Krauss wildcard-matching algorithm is a pattern matching algorithm. Based on the wildcard syntax in common use, e.g. in the Microsoft Windows command-line interface, the algorithm provides a non-recursive mechanism for matching patterns in software applications, based on syntax simpler than that typically offered by regular expressions.
Python has a glob module in the standard library which performs wildcard pattern matching on filenames, [28] and an fnmatch module with functions for matching strings or filtering lists based on these same wildcard patterns. [17] Guido van Rossum, author of the Python programming language, wrote and contributed a glob routine to BSD Unix in ...
A simple and inefficient way to see where one string occurs inside another is to check at each index, one by one. First, we see if there is a copy of the needle starting at the first character of the haystack; if not, we look to see if there's a copy of the needle starting at the second character of the haystack, and so forth.
The basic linguistic assumption of proximity searching is that the proximity of the words in a document implies a relationship between the words. Given that authors of documents try to formulate sentences which contain a single idea, or cluster of related ideas within neighboring sentences or organized into paragraphs, there is an inherent, relatively high, probability within the document ...
The (standard) Boolean model of information retrieval (BIR) [1] is a classical information retrieval (IR) model and, at the same time, the first and most-adopted one. [2] The BIR is based on Boolean logic and classical set theory in that both the documents to be searched and the user's query are conceived as sets of terms (a bag-of-words model).
In SQL, wildcard characters can be used in LIKE expressions; the percent sign % matches zero or more characters, and underscore _ a single character. Transact-SQL also supports square brackets ([and ]) to list sets and ranges of characters to match, a leading caret ^ negates the set and matches only a character not within the list.
These vectors capture information about the meaning of the word based on the surrounding words. The word2vec algorithm estimates these representations by modeling text in a large corpus . Once trained, such a model can detect synonymous words or suggest additional words for a partial sentence.