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HDT (Header, Dictionary, Triples) is a data structure and format for serialization which optimizes data compression while still making the media available for web navigation. [ 1 ] The key elements of the format are the header , the dictionary or associative array , and the semantic triple .
The most frequently used general-purpose implementation of an associative array is with a hash table: an array combined with a hash function that separates each key into a separate "bucket" of the array. The basic idea behind a hash table is that accessing an element of an array via its index is a simple, constant-time operation. Therefore, the ...
IWE combines Word2vec with a semantic dictionary mapping technique to tackle the major challenges of information extraction from clinical texts, which include ambiguity of free text narrative style, lexical variations, use of ungrammatical and telegraphic phases, arbitrary ordering of words, and frequent appearance of abbreviations and acronyms ...
In order to efficiently store dictionary data, dictzip, an extension to the gzip compression format (also the name of the utility), can be used to compress a .dict file. Dictzip compresses file in chunks and stores the chunk index in the gzip file header, thus allowing random access to the data.
In Lua, "table" is a fundamental type that can be used either as an array (numerical index, fast) or as an associative array. The keys and values can be of any type, except nil. The following focuses on non-numerical indexes. A table literal is written as { value, key = value, [index] = value, ["non id string"] = value }. For example:
The Field Names, Header Fields and Repository of Provisional Registrations are maintained by the IANA. Additional field names and permissible values may be defined by each application. Header field names are case-insensitive. [4] This is in contrast to HTTP method names (GET, POST, etc.), which are case-sensitive. [5]
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 ...
In a well-dimensioned hash table, the average time complexity for each lookup is independent of the number of elements stored in the table. Many hash table designs also allow arbitrary insertions and deletions of key–value pairs, at amortized constant average cost per operation. [3] [4] [5] Hashing is an example of a space-time tradeoff.