In any programming language that has first-class functions, parser combinators can be used to combine basic parsers to construct parsers for more complex rules. For example, a production rule of a context-free grammar (CFG) may have one or more alternatives and each alternative may consist of a sequence of non-terminal(s) and/or terminal(s), or the alternative may consist of a single non-terminal or terminal or the empty string. If a simple parser is available for each of these alternatives, a parser combinator can be used to combine each of these parsers, returning a new parser which can recognise any or all of the alternatives.
In languages that support operator overloading, a parser combinator can take the form of an infix operator, used to glue different parsers to form a complete rule. Parser combinators thereby enable parsers to be defined in an embedded style, in code which is similar in structure to the rules of the formal grammar. As such, implementations can be thought of as executable specifications with all the associated advantages such as readability.
To keep the discussion relatively straightforward, we discuss parser combinators in terms of recognizers only. If the input string is of length #input and its members are accessed through an index j, a recognizer is a parser which returns, as output, a set of indices representing indices at which the parser successfully finished recognizing a sequence of tokens that begin at index j. An empty result set indicates that the recognizer failed to recognize any sequence beginning at index j.
Given two recognizers p and q, we can define two major parser combinators, one for matching alternative rules and one for sequencing rules:
There may be multiple distinct ways to parse a string while finishing at the same index, indicating an ambiguous grammar. Simple recognizers do not acknowledge these ambiguities; each possible finishing index is listed only once in the result set. For a more complete set of results, a more complicated object such as a parse tree must be returned.
Consider a highly ambiguous context-free grammar, s ::= ‘x’ s s | ε. Using the combinators defined earlier, we can modularly define executable notations of this grammar in a modern functional programming language (e.g., Haskell) as s = term ‘x’ <*> s <*> s <+> empty. When the recognizer s is applied at index 2 of the input sequence x x x x x it would return a result set {2,3,4,5}, indicating that there were matches starting at index 2 and finishing at any index between 2 and 5 inclusive.
Parser combinators, like all recursive descent parsers, are not limited to the context-free grammars and thus do no global search for ambiguities in the LL(k) parsing Firstk and Followk sets. Thus, ambiguities are not known until run-time if and until the input triggers them. In such cases, the recursive descent parser may default (perhaps unknown to the grammar designer) to one of the possible ambiguous paths, resulting in semantic confusion (aliasing) in the use of the language. This leads to bugs by users of ambiguous programming languages, which are not reported at compile-time, and which are introduced not by human error, but by ambiguous grammar. The only solution that eliminates these bugs is to remove the ambiguities and use a context-free grammar.
The simple implementations of parser combinators have some shortcomings, which are common in top-down parsing. Naïve combinatory parsing requires exponential time and space when parsing an ambiguous context-free grammar. In 1996, Frost and Szydlowski demonstrated how memoization can be used with parser combinators to reduce the time complexity to polynomial.6 Later Frost used monads to construct the combinators for systematic and correct threading of memo-table throughout the computation.7
Like any top-down recursive descent parsing, the conventional parser combinators (like the combinators described above) will not terminate while processing a left-recursive grammar (e.g. s ::= s <*> term ‘x’|empty). A recognition algorithm that accommodates ambiguous grammars with direct left-recursive rules is described by Frost and Hafiz in 2006.8 The algorithm curtails the otherwise ever-growing left-recursive parse by imposing depth restrictions. That algorithm was extended to a complete parsing algorithm to accommodate indirect as well as direct left-recursion in polynomial time, and to generate compact polynomial-size representations of the potentially exponential number of parse trees for highly ambiguous grammars by Frost, Hafiz and Callaghan in 2007.9 This extended algorithm accommodates indirect left recursion by comparing its ‘computed context’ with ‘current context’. The same authors also described their implementation of a set of parser combinators written in the Haskell language based on the same algorithm.1011
Frost & Launchbury 1989. - Frost, Richard; Launchbury, John (1989). "Constructing natural language interpreters in a lazy functional language" (PDF). The Computer Journal. Special edition on Lazy Functional Programming. 32 (2): 108–121. doi:10.1093/comjnl/32.2.108. Archived from the original on 2013-06-06. https://web.archive.org/web/20130606162214/https://courses.cit.cornell.edu/ling4424/frost-launchbury.pdf ↩
Hutton 1992. - Hutton, Graham (1992). "Higher-order functions for parsing". Journal of Functional Programming. 2 (3): 323–343. CiteSeerX 10.1.1.34.1287. doi:10.1017/s0956796800000411. S2CID 31067887. https://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.34.1287 ↩
Hutton, Graham; Meijer, Erik. Monadic Parser Combinators (PDF) (Report). University of Nottingham. Retrieved 13 February 2023. http://www.cs.nott.ac.uk/~pszgmh/monparsing.pdf ↩
Swierstra 2001. - Swierstra, S. Doaitse (2001). "Combinator parsers: From toys to tools". Electronic Notes in Theoretical Computer Science. 41: 38–59. doi:10.1016/S1571-0661(05)80545-6. https://doi.org/10.1016%2FS1571-0661%2805%2980545-6 ↩
Frost, Hafiz & Callaghan 2008. - Frost, Richard A.; Hafiz, Rahmatullah; Callaghan, Paul (2008). "Parser Combinators for Ambiguous Left-Recursive Grammars". Practical Aspects of Declarative Languages. ACM-SIGPLAN. Vol. 4902. pp. 167–181. CiteSeerX 10.1.1.89.2132. doi:10.1007/978-3-540-77442-6_12. ISBN 978-3-540-77441-9. https://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.89.2132 ↩
Frost & Szydlowski 1996. - Frost, Richard A.; Szydlowski, Barbara (1996). "Memoizing Purely Functional Top-Down Backtracking Language Processors" (PDF). Sci. Comput. Program. 27 (3): 263–288. doi:10.1016/0167-6423(96)00014-7. http://cs.uwindsor.ca/~richard/PUBLICATIONS/SCOMP_96.pdf ↩
Frost 2003. - Frost, Richard A. (2003). "Monadic Memoization towards Correctness-Preserving Reduction of Search". Proceedings of the 16th Canadian Society for Computational Studies of Intelligence Conference on Advances in Artificial Intelligence (AI'03) (PDF). Springer. pp. 66–80. ISBN 978-3-540-40300-5. http://cs.uwindsor.ca/~richard/PUBLICATIONS/AI_03.pdf ↩
Frost & Hafiz 2006. - Frost, Richard A.; Hafiz, Rahmatullah (2006). "A New Top-Down Parsing Algorithm to Accommodate Ambiguity and Left Recursion in Polynomial Time" (PDF). ACM SIGPLAN Notices. 41 (5): 46–54. doi:10.1145/1149982.1149988. S2CID 8006549. http://cs.uwindsor.ca/~hafiz/pub/p46-frost.pdf ↩
Frost, Hafiz & Callaghan 2007. - Frost, Richard A.; Hafiz, Rahmatullah; Callaghan, Paul (2007). "Modular and Efficient Top-Down Parsing for Ambiguous Left-Recursive Grammars". Proceedings of the 10th International Workshop on Parsing Technologies (IWPT), ACL-SIGPARSE: 109–120. CiteSeerX 10.1.1.97.8915. https://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.97.8915 ↩
cf. X-SAIGA — executable specifications of grammars http://www.cs.uwindsor.ca/~hafiz/proHome.html ↩