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In information theory, linguistics, and computer science, the Levenshtein distance is a string metric for measuring the difference between two sequences. The Levenshtein distance between two words is the minimum number of single-character edits (insertions, deletions or substitutions) required to change one word into the other.
Like raw strings, there can be any number of equals signs between the square brackets, provided both the opening and closing tags have a matching number of equals signs; this allows nesting as long as nested block comments/raw strings use a different number of equals signs than their enclosing comment: --[[comment --[=[ nested comment ...
The zero-width space can be used to mark word breaks in languages without visible space between words, such as Thai, Myanmar, Khmer, and Japanese. [ 1 ] In justified text, the rendering engine may add inter-character spacing, also known as letter spacing, between letters separated by a zero-width space, unlike around fixed-width spaces.
Rexx uses this syntax for concatenation including an intervening space. C (along with Python) allows juxtaposition for string literals, however, for strings stored as character arrays, the strcat function must be used. COBOL uses the STRING statement to concatenate string variables. MATLAB and Octave use the syntax "[x y]" to concatenate x and y.
Various algorithms exist that solve problems beside the computation of distance between a pair of strings, to solve related types of problems. Hirschberg's algorithm computes the optimal alignment of two strings, where optimality is defined as minimizing edit distance. Approximate string matching can be formulated in terms of edit distance.
The longest common substrings of a set of strings can be found by building a generalized suffix tree for the strings, and then finding the deepest internal nodes which have leaf nodes from all the strings in the subtree below it. The figure on the right is the suffix tree for the strings "ABAB", "BABA" and "ABBA", padded with unique string ...
This facet of word2vec has been exploited in a variety of other contexts. For example, word2vec has been used to map a vector space of words in one language to a vector space constructed from another language. Relationships between translated words in both spaces can be used to assist with machine translation of new words. [27]
Irish scribes first started to add word spacing to texts in the late 7th century, creating what Paul Sänger, in his book The Spaces between the Words, refers to as aerated text. By the 11th century, scribes in northern Europe were separating Latin text canonically , that is, with spaces between words, just as we do today in standard written text.