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For example, one could define a dictionary having a string "toast" mapped to the integer 42 or vice versa. The keys in a dictionary must be of an immutable Python type, such as an integer or a string, because under the hood they are implemented via a hash function. This makes for much faster lookup times, but requires keys not change.
The intuition is that the closer two words or synsets are, the closer their meaning. A number of WordNet-based word similarity algorithms are implemented in a Perl package called WordNet::Similarity, [21] and in a Python package called NLTK. [22]
words is a standard file on Unix and Unix-like operating systems, and is simply a newline-delimited list of dictionary words. It is used, for instance, by spell-checking programs. [1] The words file is usually stored in /usr/share/dict/words or /usr/dict/words.
The two characters commonly used for this purpose are the hyphen ("-") and the underscore ("_"); e.g., the two-word name "two words" would be represented as "two-words" or "two_words". The hyphen is used by nearly all programmers writing COBOL (1959), Forth (1970), and Lisp (1958); it is also common in Unix for commands and packages, and is ...
Oxford Dictionary has 273,000 headwords; 171,476 of them being in current use, 47,156 being obsolete words and around 9,500 derivative words included as subentries. The dictionary contains 157,000 combinations and derivatives, and 169,000 phrases and combinations, making a total of over 600,000 word-forms. [37] [38]
The syntax is keyword1 near:n keyword2 where n=the number of maximum separating words. Ordered search within the Google and Yahoo! search engines is possible using the asterisk (*) full-word wildcards: in Google this matches one or more words, [9] and an in Yahoo! Search this matches exactly one word. [10] (This is easily verified by searching ...
A dictionary coder, also sometimes known as a substitution coder, is a class of lossless data compression algorithms which operate by searching for matches between the text to be compressed and a set of strings contained in a data structure (called the 'dictionary') maintained by the encoder. When the encoder finds such a match, it substitutes ...
The final step for the BoW model is to convert vector-represented patches to "codewords" (analogous to words in text documents), which also produces a "codebook" (analogy to a word dictionary). A codeword can be considered as a representative of several similar patches. One simple method is performing k-means clustering over all the vectors. [7]