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from Dictionary.com:
parse
/pɑrs, pɑrz/
Show Spelled Pronunciation [pahrs, pahrz]
Show IPA verb, parsed, pars⋅ing. –verb (used with object) 1. to analyze (a sentence) in terms of grammatical constituents, identifying the parts of speech, syntactic relations, etc. 2. to describe (a word in a sentence) grammatically, identifying the part of speech, inflectional form, syntactic function, etc. 3.
Computers. to analyze (a string of characters) in order to associate groups of characters with the syntactic units of the underlying grammar.
–verb (used without object) 4. to admit of being parsed.
Origin:
1545–55; < L pars part, as in pars ōrātiōnis part of speech
Related forms:
pars⋅a⋅ble, adjective
parser, noun
Dictionary.com Unabridged
Based on the Random House Dictionary, © Random House, Inc. 2009.
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parse
parse (pärs)
v.
parsed,
pars·ing,
pars·es
v.
tr.
- To break (a sentence) down into its component parts of speech with an explanation of the form, function, and syntactical relationship of each part.
- To describe (a word) by stating its part of speech, form, and syntactical relationships in a sentence.
- To examine closely or subject to detailed analysis, especially by breaking up into components: "What are we missing by parsing the behavior of chimpanzees into the conventional categories recognized largely from our own behavior?" (Stephen Jay Gould).
- To make sense of; comprehend: I simply couldn't parse what you just said.
- Computer Science To analyze or separate (input, for example) into more easily processed components.
v.
intr.
To admit of being parsed:
sentences that do not parse easily.
[Probably from Middle English pars,
part of speech, from Latin pars (ōrātiōnis),
part (of speech); see perə-2 in Indo-European roots.]
pars'er n.
The American Heritage® Dictionary of the English Language, Fourth Edition
Copyright © 2009 by Houghton Mifflin Company.
Published by Houghton Mifflin Company. All rights reserved.
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Parse
Parse\, v. t. [imp. & p. p.
Parsed; p.
pr. &
vb. n.
Parsing.] [L. pars a part; pars orationis a part of speech. See
Part, n.] (Gram.) To resolve into its elements, as a sentence, pointing out the several parts of speech, and their relation to each other by government or agreement; to analyze and describe grammatically. Let him construe the letter into English, and parse it over perfectly. --Ascham.
Webster's Revised Unabridged Dictionary, © 1996, 1998 MICRA, Inc.
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parse
[from linguistic terminology] vt.
1. To determine the syntactic structure of a sentence or other utterance (close to the standard English meaning). "That was the one I saw you." "I can't parse that."
2. More generally, to understand or comprehend. "It's very simple; you just kretch the glims and then aos the zotz." "I can't parse that."
3. Of fish, to have to remove the bones yourself. "I object to parsing fish", means "I don't want to get a whole fish, but a sliced one is okay". A `parsed fish' has been deboned. There is some controversy over whether `unparsed' should mean `bony', or also mean `deboned'.
Jargon File 4.2.0
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parse
c.1553, "to state the parts of speech in a sentence," verb use of M.E. pars (n.) "part of speech" (c.1300), from O.Fr. pars, pl. of part "part," from L. pars (see part (n.)) in school question, Quae pars orationis? "What part of speech?"
Online Etymology Dictionary, © 2001 Douglas Harper
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From Reference.com:
parsing
In
computer science and
linguistics,
parsing, or, more formally,
syntactic analysis, is the process of analyzing a sequence of
tokens to determine their grammatical structure with respect to a given (more or less)
formal grammar.
Parsing is also an earlier term for the diagramming of sentences of natural languages, and is still used for the diagramming of
inflected languages, such as the
Romance languages or
Latin. The term parsing comes from Latin
pars (
ōrātiōnis), meaning
part (of speech).
Parser
A
parser is one of the components in an
interpreter or
compiler, which checks for correct syntax and builds a
data structure (often some kind of
parse tree,
abstract syntax tree or other hierarchical structure) implicit in the input tokens. The parser often uses a separate
lexical analyser to create tokens from the sequence of input characters. Parsers may be programmed by hand or may be semi-automatically generated (in some programming language) by a tool (such as
Yacc) from a grammar written in
Backus-Naur form.
Human languages
In some
machine translation and
natural language processing systems, human languages are parsed by computer programs. Human sentences are not easily parsed by programs, as there is substantial
ambiguity in the structure of human language. In order to parse natural language data, researchers must first agree on the
grammar to be used. The choice of syntax is affected by both
linguistic and computational concerns; for instance some parsing systems use
lexical functional grammar, but in general, parsing for grammars of this type is known to be
NP-complete.
Head-driven phrase structure grammar is another linguistic formalism which has been popular in the parsing community, but other research efforts have focused on less complex formalisms such as the one used in the Penn
Treebank.
Shallow parsing aims to find only the boundaries of major constituents such as noun phrases. Another popular strategy for avoiding linguistic controversy is
dependency grammar parsing.
Most modern parsers are at least partly
statistical; that is, they rely on a corpus of training data which has already been annotated (parsed by hand). This approach allows the system to gather information about the frequency with which various constructions occur in specific contexts.
(See machine learning.) Approaches which have been used include straightforward
PCFGs (probabilistic context free grammars),
maximum entropy, and
neural nets. Most of the more successful systems use
lexical statistics (that is, they consider the identities of the words involved, as well as their
part of speech). However such systems are vulnerable to
overfitting and require some kind of smoothing to be effective.
Parsing algorithms for natural language cannot rely on the grammar having 'nice' properties as with manually-designed grammars for programming languages. As mentioned earlier some grammar formalisms are very computationally difficult to parse; in general, even if the desired structure is not
context-free, some kind of context-free approximation to the grammar is used to perform a first pass. Algorithms which use context-free grammars often rely on some variant of the
CKY algorithm, usually with some
heuristic to prune away unlikely analyses to save time.
(See chart parsing.) However some systems trade speed for accuracy using, eg, linear-time versions of the
shift-reduce algorithm. A somewhat recent development has been
parse reranking in which the parser proposes some large number of analyses, and a more complex system selects the best option.
Programming languages
The most common use of a parser is as a component of a
compiler or
interpreter. This parses the
source code of a
computer programming language to create some form of internal representation. Programming languages tend to be specified in terms of a
context-free grammar because fast and efficient parsers can be written for them. Parsers are written by hand or generated by
parser generators.
Context-free grammars are limited in the extent to which they can express all of the requirements of a language. Informally, the reason is that the memory of such a language is limited. The grammar cannot remember the presence of a construct over an arbitrarily long input; this is necessary for a language in which, for example, a name must be declared before it may be referenced. More powerful grammars that can express this constraint, however, cannot be parsed efficiently. Thus, it is a common strategy to create a relaxed parser for a context-free grammar which accepts a superset of the desired language constructs (that is, it accepts some invalid constructs); later, the unwanted constructs can be filtered out.
Overview of process
The following example demonstrates the common case of parsing a computer language with two levels of grammar: lexical and syntactic.
The first stage is the token generation, or
lexical analysis, by which the input character stream is split into meaningful symbols defined by a grammar of
regular expressions. For example, a calculator program would look at an input such as "12*(3+4)^2" and split it into the tokens 12, *, (, 3, +, 4, ), ^, and 2, each of which is a meaningful symbol in the context of an arithmetic expression. The parser would contain rules to tell it that the characters *, +, ^, ( and ) mark the start of a new token, so meaningless tokens like "12*" or "(3" will not be generated.
The next stage is parsing or syntactic analysis, which is checking that the tokens form an allowable expression. This is usually done with reference to a
context-free grammar which recursively defines components that can make up an expression and the order in which they must appear. However, not all rules defining programming languages can be expressed by context-free grammars alone, for example type validity and proper declaration of identifiers. These rules can be formally expressed with
attribute grammars.
The final phase is
semantic parsing or analysis, which is working out the implications of the expression just validated and taking the appropriate action. In the case of a calculator or interpreter, the action is to evaluate the expression or program; a compiler, on the other hand, would generate some kind of code. Attribute grammars can also be used to define these actions.
Types of parsers
The task of the parser is essentially to determine if and how the input can be derived from the start symbol of the grammar. This can be done in essentially two ways:
- Top-down parsing - Top-down parsing can be viewed as an attempt to find left-most derivations of an input-stream by searching for parse trees using a top-down expansion of the given formal grammar rules. Tokens are consumed from left to right. Inclusive choice is used to accommodate ambiguity by expanding all alternative right-hand-sides of grammar rules . LL parsers and recursive-descent parser are examples of top-down parsers, which cannot accommodate left recursive productions. Although it has been believed that simple implementations of top-down parsing cannot accommodate direct and indirect left-recursion and may require exponential time and space complexity while parsing ambiguous context-free grammars, more sophisticated algorithm for top-down parsing have been created by Frost, Hafiz, and Callaghan which accommodates ambiguity and left recursion in polynomial time and which generates polynomial-size representations of the potentially-exponential number of parse trees. Their algorithm is able to produce both left-most and right-most derivations of an input with regard to a given CFG.
- Bottom-up parsing - A parser can start with the input and attempt to rewrite it to the start symbol. Intuitively, the parser attempts to locate the most basic elements, then the elements containing these, and so on. LR parsers are examples of bottom-up parsers. Another term used for this type of parser is Shift-Reduce parsing.
Another important distinction is whether the parser generates a
leftmost derivation or a
rightmost derivation (see
context-free grammar). LL parsers will generate a leftmost
derivation and LR parsers will generate a rightmost derivation (although usually in reverse) .
Examples of parsers
Top-down parsers
Some of the parsers that use
top-down parsing include:
- Recursive descent parser
- LL parser (Left-to-right, Leftmost derivation)
- X-SAIGA - eXecutable SpecificAtIons of GrAmmars. Contains publications related to top-down parsing algorithm that supports left-recursion and ambiguity in polynomial time and space.
Bottom-up parsers
Some of the parsers that use
bottom-up parsing include:
- Precedence parser
- BC (bounded context) parsing
- LR parser (Left-to-right, Rightmost derivation)
- CYK parser
Parser development software
Some of the well known parser development tools include the following. Also see
comparison of parser generators.
See also
Parsing concepts
References
Further reading
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No thanks, I'll parse
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I hope this clarified "parse". Now onto "algorithm"....
maddog
Re: Algorithms beyond programming
Re: Algorithms beyond programming
Re: Algorithms beyond programming
I must disagree with Buffy on matters of this kind!
