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). [] []


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.Fact|date=May 2008

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 Aho, A.V., Sethi, R. and Ullman ,J.D. (1986) " Compilers: principles, techniques, and tools." " Addison-Wesley Longman Publishing Co., Inc. Boston, MA, USA. " ] . 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 Frost, R., Hafiz, R. and Callaghan, P. (2007) " Modular and Efficient Top-Down Parsing for Ambiguous Left-Recursive Grammars ." "10th International Workshop on Parsing Technologies (IWPT), ACL-SIGPARSE ", Pages: 109 - 120, June 2007, Prague.] Frost, R., Hafiz, R. and Callaghan, P. (2008) " Parser Combinators for Ambiguous Left-Recursive Grammars." " 10th International Symposium on Practical Aspects of Declarative Languages (PADL), ACM-SIGPLAN ", Volume 4902/2008, Pages: 167-181, January 2008, San Francisco.] 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) Fact|date=January 2008.

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
** Operator-precedence parser
** Simple precedence parser
* BC (bounded context) parsing
* LR parser (Left-to-right, Rightmost derivation)
** Simple LR (SLR) parser
** LALR parser
** Canonical LR (LR(1)) parser
** GLR parser
* CYK parser

Parser development software

Some of the well known parser development tools include the following. Also see comparison of parser generators.

* Bison
* Coco/R
* JavaCC
* Lex
* Ragel
* Spirit Parser Framework
* Yacc
* Lemon

See also

* Generating strings

Parsing concepts

* Chart parser
* Compiler-compiler
* Deterministic parsing
* Lexing
* Shallow parsing


Further reading

* [ Parsing Techniques - A Practical Guide] web page of book includes downloadable pdf.

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