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Frequency lists are a useful tool when building an electronic dictionary, which is a prerequisite for a wide range of applications in computational linguistics. German linguists define the Häufigkeitsklasse (frequency class) N {\displaystyle N} of an item in the list using the base 2 logarithm of the ratio between its frequency and the ...
Beyond reserving specific lists of words, some languages reserve entire ranges of words, for use as private spaces for future language version, different dialects, compiler vendor-specific extensions, or for internal use by a compiler, notably in name mangling. This is most often done by using a prefix, often one or more underscores.
This is a list of dictionaries considered authoritative or complete by approximate number of total words, or headwords, included. number of words in a language. [1] [2] In compiling a dictionary, a lexicographer decides whether the evidence of use is sufficient to justify an entry in the dictionary. This decision is not the same as determining ...
Python sets are very much like mathematical sets, and support operations like set intersection and union. Python also features a frozenset class for immutable sets, see Collection types. Dictionaries (class dict) are mutable mappings tying keys and corresponding values. Python has special syntax to create dictionaries ({key: value})
Python and Ruby both recommend UpperCamelCase for class names, CAPITALIZED_WITH_UNDERSCORES for constants, and snake_case for other names. In Python, if a name is intended to be "private", it is prefixed by one or two underscores. Private variables are enforced in Python only by convention.
Word2vec is a technique in natural language processing (NLP) for obtaining vector representations of words. These vectors capture information about the meaning of the word based on the surrounding words.
The NIST Dictionary of Algorithms and Data Structures [1] is a reference work maintained by the U.S. National Institute of Standards and Technology. It defines a large number of terms relating to algorithms and data structures. For algorithms and data structures not necessarily mentioned here, see list of algorithms and list of data structures.
which shows which documents contain which terms and how many times they appear. Note that, unlike representing a document as just a token-count list, the document-term matrix includes all terms in the corpus (i.e. the corpus vocabulary), which is why there are zero-counts for terms in the corpus which do not also occur in a specific document.