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Examples: An empty dictionary is encoded as de. A dictionary with keys "wiki" → "bencode" and "meaning" → 42 is encoded as d7:meaningi42e4:wiki7:bencodee. There are no restrictions on the types of values stored within lists and dictionaries; they may contain other lists and dictionaries, allowing for arbitrarily complex data structures.
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.
Dictionaries are represented as: { "key" = "value"; ... }. The left-hand side must be a string, but it can be unquoted. Comments are allowed as: /* This is a comment */ and // This is a line comment. As in C, whitespace are generally insignificant to syntax. Value statements terminate by a semicolon.
^ ASN.1 has X.681 (Information Object System), X.682 (Constraints), and X.683 (Parameterization) that allow for the precise specification of open types where the types of values can be identified by integers, by OIDs, etc. OIDs are a standard format for globally unique identifiers, as well as a standard notation ("absolute reference") for ...
find the value (if any) that is bound to a given key. The argument to this operation is the key, and the value is returned from the operation. If no value is found, some lookup functions raise an exception, while others return a default value (such as zero, null, or a specific value passed to the constructor).
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 ...
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 ...
It is intended to be easy to read and write due to obvious semantics which aim to be "minimal", and it is designed to map unambiguously to a dictionary. Originally created by Tom Preston-Werner, its specification is open source. TOML is used in a number of software projects [4] [5] [6] and is implemented in many programming languages. [7]