Parser Data
What Is Parsing of Data? – Blog | Oxylabs
If you work with development (whether part of the team or work in a company where you need to communicate with the tech team often), you’ll most likely come across the term data parsing. Simply put, it’s a process when one data format is transformed into another, more readable data format. But that’s a rather straightforward explanation.
In this article we’ll dig a little deeper on what is parsing of data, and discuss whether building an in-house data parser is more beneficial to a business, or is it better to buy a data extraction solution that already does the parsing for you.
What is data parsing?
Data parsing is a widely used method for data structuring; thus, you may discover many different descriptions while trying to find out what exactly it is. To make understanding this concept easier, we’ve put it into a simple definition.
What is data parsing? Data parsing is a method where one string of data gets converted into a different type of data. So let’s say you receive your data in raw HTML, a parser will take the said HTML and transform it into a more readable data format that can be easily read and understood.
What does a parser do?
A well-made parser will distinguish which information of the HTML string is needed, and in accordance to the parsers pre-written code and rules, it will pick out the necessary information and convert it into JSON, CSV or a table, for example.
It’s important to mention that a parser itself is not tied to a data format. It’s a tool that converts one data format into another, how it converts it and into what depends on how the parser was built.
Parsers are used for many technologies, including:
Java and other programming languagesHTML and XMLInteractive data language and object definition languageSQL and other database languagesModeling languagesScripting languagesHTTP and other internet protocols
To build or to buy?
Now, when it comes to the business side of things, an excellent question to ask yourself is, “Should my tech team build their own parser, or should we simply outsource? ”
As a rule of thumb, it’s usually cheaper to build your own, rather than to buy a premade tool. However, this isn’t an easy question to answer, and a lot more things should be taken into consideration when deciding to build or to buy.
Let’s look into the possibilities and outcomes with both options.
Building a data parser
Let’s say you decide to build your own parser. There are a few distinct benefits if making this decision:
A parser can be anything you like. It can be tailor-made for any work (parsing) you require. It’s usually cheaper to build your own ’re in control whatever decisions need to be made when updating and maintaining your parser.
But, like with anything, there’s always a downside of building your own parser:
You’ll need to hire and train a whole in-house team to build the intaining the parser is necessary – meaning more in house expenses and time resources ’ll need to buy and build a server that will be fast enough to parse your data in the speed you in control isn’t necessarily easy or beneficial – you’ll need to work closely with the tech team to make the right decisions to create something good, spending a lot of your time planning and testing.
Building your own has its benefits – but it takes a lot of your resources and time. Especially if you need to develop a sophisticated parser for parsing large volumes. That will require more maintenance and human resources, and valuable human resources because building one will require a highly-skilled developer team.
Buying a data parser
So what about buying a tool that parses your data for you? Let’s start with the benefits:
You won’t need to spend any money on human resources, as everything will be done for you, including maintaining the parser and the issues that arise will be solved a lot faster, as the people you buy your tools from have extensive know-how and are familiarized with their technology. It’s also less likely that the parser will crash or experience issues in general, as it will be tested and perfected to fit the markets’ requirements. You’ll save a lot on human resources and your own time, as the decision making on how to build the best parser will come from the outsourcing.
Of course, there are a few downsides to buying a parser as well:
It will be slightly more won’t have too much control over it.
Now, it seems that there are a lot of benefits to simply just buy one. But one thing that might make things easier to choose is to consider what sort of parser you’ll need. An expert developer can make an easy parser probably within a week. But if it’s a complex one, it can take months – that’s a lot of time, and resources.
It also falls to whether you’re a big business that has a lot of time and resources on their hands to build and maintain a parser. Or you’re a smaller business that needs to get things done to be able to grow within the market.
How we do it: Real-Time Crawler
Here at Oxylabs, we have a data gathering tool called Real-Time Crawler. This product is specifically built to scrape search engines and e-commerce websites on a large scale. We covered what Real-Time Crawler is and how it works in great detail in one of our articles, so make sure to check it out. Also, here’s a video below:
But why are we bringing up this tool? Well, Real-Time Crawler not only gathers the data – it also has a built-in parser that turns your HTML into JSON. If you choose to use Real-Time Crawler Callback method, after every job request, you’ll be provided with a URL to download the results in HTML or parsed JSON format.
Our built-in parser handles quite a lot of data daily. On February, 12 billion requests were made! And that’s back in February! Based on our 2019, Q1 statistics, the total requests grew by 7. 02% in comparison to Q4 2018. And these numbers continue to rise in accordance in Q2, 2019.
Our tech team has been working with this project for a few years now, and having this much experience we can say with confidence that the parser we built can handle any volume of data one might request.
So – to build or to buy? Well, building several years of experience, improvements, and maintenance of a tool that does its job to perfection – honestly, quite expensive.
Wrapping up
Hopefully, now you have a decent understanding of what is parsing of data. Taking everything into account, keep in mind whether you’re building a very sophisticated parser or not. If you are parsing large volumes of data, you will need good developers on your team to develop and maintain the parser. But, if you need a less complicated, smaller parser – probably best to build your own.
Also be mindful if you are a large company with a lot of resources, or a smaller one, that needs the right tools to keep things growing.
Oxylabs’ clients have significantly increased growth with Real-Time Crawler! If you are also looking for ways to improve your business, register here to start using our tools. Also, if you have more questions about data parsing, book a call with our sales team!
People also ask
What tools are required for data parsing?
After web scraping tools provide the required data, there are several options for data parsing. BeautifulSoup and LXML are two commonly used data parsing tools.
How to use a data parser?
Every data parsing tool will come with its own manual. Most of them will require some technical knowledge such as understanding Python and data from a web scraper.
What is data scraping?
Data scraping is the process of acquiring large amounts of data from the web through the use of automation and rotating IP address.
Gabija Fatenaite is a Product Marketing Manager at Oxylabs. Having grown up on video games and the internet, she grew to find the tech side of things more and more interesting over the years. So if you ever find yourself wanting to learn more about proxies (or video games), feel free to contact her – she’ll be more than happy to answer you.
All information on Oxylabs Blog is provided on an “as is” basis and for informational purposes only. We make no representation and disclaim all liability with respect to your use of any information contained on Oxylabs Blog or any third-party websites that may be linked therein. Before engaging in scraping activities of any kind you should consult your legal advisors and carefully read the particular website’s terms of service or receive a scraping license.
Parsing – Wikipedia
Parsing, syntax analysis, or syntactic analysis is the process of analyzing a string of symbols, either in natural language, computer languages or data structures, conforming to the rules of a formal grammar. The term parsing comes from Latin pars (orationis), meaning part (of speech). [1]
The term has slightly different meanings in different branches of linguistics and computer science. Traditional sentence parsing is often performed as a method of understanding the exact meaning of a sentence or word, sometimes with the aid of devices such as sentence diagrams. It usually emphasizes the importance of grammatical divisions such as subject and predicate.
Within computational linguistics the term is used to refer to the formal analysis by a computer of a sentence or other string of words into its constituents, resulting in a parse tree showing their syntactic relation to each other, which may also contain semantic and other information (p-values). [citation needed] Some parsing algorithms may generate a parse forest or list of parse trees for a syntactically ambiguous input. [2]
The term is also used in psycholinguistics when describing language comprehension. In this context, parsing refers to the way that human beings analyze a sentence or phrase (in spoken language or text) “in terms of grammatical constituents, identifying the parts of speech, syntactic relations, etc. “[1] This term is especially common when discussing what linguistic cues help speakers to interpret garden-path sentences.
Within computer science, the term is used in the analysis of computer languages, referring to the syntactic analysis of the input code into its component parts in order to facilitate the writing of compilers and interpreters. The term may also be used to describe a split or separation.
Human languages[edit]
Traditional methods[edit]
The traditional grammatical exercise of parsing, sometimes known as clause analysis, involves breaking down a text into its component parts of speech with an explanation of the form, function, and syntactic relationship of each part. [3] This is determined in large part from study of the language’s conjugations and declensions, which can be quite intricate for heavily inflected languages. To parse a phrase such as ‘man bites dog’ involves noting that the singular noun ‘man’ is the subject of the sentence, the verb ‘bites’ is the third person singular of the present tense of the verb ‘to bite’, and the singular noun ‘dog’ is the object of the sentence. Techniques such as sentence diagrams are sometimes used to indicate relation between elements in the sentence.
Parsing was formerly central to the teaching of grammar throughout the English-speaking world, and widely regarded as basic to the use and understanding of written language. However, the general teaching of such techniques is no longer current. [citation needed]
Computational methods[edit]
In some machine translation and natural language processing systems, written texts in human languages are parsed by computer programs. [4] Human sentences are not easily parsed by programs, as there is substantial ambiguity in the structure of human language, whose usage is to convey meaning (or semantics) amongst a potentially unlimited range of possibilities but only some of which are germane to the particular case. [5] So an utterance “Man bites dog” versus “Dog bites man” is definite on one detail but in another language might appear as “Man dog bites” with a reliance on the larger context to distinguish between those two possibilities, if indeed that difference was of concern. It is difficult to prepare formal rules to describe informal behaviour even though it is clear that some rules are being followed. [citation needed]
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), [6] maximum entropy, [7] and neural nets. [8] 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. [citation needed]
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 difficult to parse computationally; 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 CYK algorithm, usually with some heuristic to prune away unlikely analyses to save time. (See chart parsing. ) However some systems trade speed for accuracy using, e. g., 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. [citation needed] Semantic parsers convert texts into representations of their meanings. [9]
Psycholinguistics[edit]
In psycholinguistics, parsing involves not just the assignment of words to categories (formation of ontological insights), but the evaluation of the meaning of a sentence according to the rules of syntax drawn by inferences made from each word in the sentence (known as connotation). This normally occurs as words are being heard or read. Consequently, psycholinguistic models of parsing are of necessity incremental, meaning that they build up an interpretation as the sentence is being processed, which is normally expressed in terms of a partial syntactic structure. Creation of initially wrong structures occurs when interpreting garden-path sentences.
Discourse analysis[edit]
Discourse analysis examines ways to analyze language use and semiotic events. Persuasive language may be called rhetoric.
Computer languages[edit]
Parser[edit]
A parser is a software component that takes input data (frequently text) and builds a data structure – often some kind of parse tree, abstract syntax tree or other hierarchical structure, giving a structural representation of the input while checking for correct syntax. The parsing may be preceded or followed by other steps, or these may be combined into a single step. The parser is often preceded by a separate lexical analyser, which creates tokens from the sequence of input characters; alternatively, these can be combined in scannerless parsing. Parsers may be programmed by hand or may be automatically or semi-automatically generated by a parser generator. Parsing is complementary to templating, which produces formatted output. These may be applied to different domains, but often appear together, such as the scanf/printf pair, or the input (front end parsing) and output (back end code generation) stages of a compiler.
The input to a parser is often text in some computer language, but may also be text in a natural language or less structured textual data, in which case generally only certain parts of the text are extracted, rather than a parse tree being constructed. Parsers range from very simple functions such as scanf, to complex programs such as the frontend of a C++ compiler or the HTML parser of a web browser. An important class of simple parsing is done using regular expressions, in which a group of regular expressions defines a regular language and a regular expression engine automatically generating a parser for that language, allowing pattern matching and extraction of text. In other contexts regular expressions are instead used prior to parsing, as the lexing step whose output is then used by the parser.
The use of parsers varies by input. In the case of data languages, a parser is often found as the file reading facility of a program, such as reading in HTML or XML text; these examples are markup languages. In the case of programming languages, a parser is a component of a compiler or interpreter, which parses the source code of a computer programming language to create some form of internal representation; the parser is a key step in the compiler frontend. Programming languages tend to be specified in terms of a deterministic context-free grammar because fast and efficient parsers can be written for them. For compilers, the parsing itself can be done in one pass or multiple passes – see one-pass compiler and multi-pass compiler.
The implied disadvantages of a one-pass compiler can largely be overcome by adding fix-ups, where provision is made for code relocation during the forward pass, and the fix-ups are applied backwards when the current program segment has been recognized as having been completed. An example where such a fix-up mechanism would be useful would be a forward GOTO statement, where the target of the GOTO is unknown until the program segment is completed. In this case, the application of the fix-up would be delayed until the target of the GOTO was recognized. Conversely, a backward GOTO does not require a fix-up, as the location will already be known.
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 at the semantic analysis (contextual analysis) step.
For example, in Python the following is syntactically valid code:
The following code, however, is syntactically valid in terms of the context-free grammar, yielding a syntax tree with the same structure as the previous, but is syntactically invalid in terms of the context-sensitive grammar, which requires that variables be initialized before use:
Rather than being analyzed at the parsing stage, this is caught by checking the values in the syntax tree, hence as part of semantic analysis: context-sensitive syntax is in practice often more easily analyzed as semantics.
Overview of process[edit]
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, ), ^, 2, each of which is a meaningful symbol in the context of an arithmetic expression. The lexer 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. [10] 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[edit]
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. [11] This is known as the primordial soup approach. Very similar to sentence diagramming, primordial soup breaks down the constituencies of sentences. [12]
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.
LL parsers and recursive-descent parser are examples of top-down parsers which cannot accommodate left recursive production rules. 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 algorithms for top-down parsing have been created by Frost, Hafiz, and Callaghan[13][14] which accommodate ambiguity and left recursion in polynomial time and which generate 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 context-free grammar.
An important distinction with regard to parsers is whether a 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). [11]
Some graphical parsing algorithms have been designed for visual programming languages. [15][16] Parsers for visual languages are sometimes based on graph grammars. [17]
Adaptive parsing algorithms have been used to construct “self-extending” natural language user interfaces. [18]
Parser development software[edit]
Some of the well known parser development tools include the following:
ANTLR
Bison
Coco/R
Definite clause grammar
GOLD
JavaCC
Lemon
Lex
LuZc
Parboiled
Parsec
Ragel
Spirit Parser Framework
Syntax Definition Formalism
SYNTAX
XPL
Yacc
PackCC
Lookahead[edit]
C program that cannot be parsed with less than 2 token lookahead. Top: C grammar excerpt. [19] Bottom: a parser has digested the tokens “int v;main(){” and is about choose a rule to derive Stmt. Looking only at the first lookahead token “v”, it cannot decide which of both alternatives for Stmt to choose; the latter requires peeking at the second token.
Lookahead establishes the maximum incoming tokens that a parser can use to decide which rule it should use. Lookahead is especially relevant to LL, LR, and LALR parsers, where it is often explicitly indicated by affixing the lookahead to the algorithm name in parentheses, such as LALR(1).
Most programming languages, the primary target of parsers, are carefully defined in such a way that a parser with limited lookahead, typically one, can parse them, because parsers with limited lookahead are often more efficient. One important change[citation needed] to this trend came in 1990 when Terence Parr created ANTLR for his Ph. D. thesis, a parser generator for efficient LL(k) parsers, where k is any fixed value.
LR parsers typically have only a few actions after seeing each token. They are shift (add this token to the stack for later reduction), reduce (pop tokens from the stack and form a syntactic construct), end, error (no known rule applies) or conflict (does not know whether to shift or reduce).
Lookahead has two advantages. [clarification needed]
It helps the parser take the correct action in case of conflicts. For example, parsing the if statement in the case of an else clause.
It eliminates many duplicate states and eases the burden of an extra stack. A C language non-lookahead parser will have around 10, 000 states. A lookahead parser will have around 300 states.
Example: Parsing the Expression 1 + 2 * 3[dubious – discuss]
Set of expression parsing rules (called grammar) is as follows,
Rule1:
E → E + E
Expression is the sum of two expressions.
Rule2:
E → E * E
Expression is the product of two expressions.
Rule3:
E → number
Expression is a simple number
Rule4:
+ has less precedence than *
Most programming languages (except for a few such as APL and Smalltalk) and algebraic formulas give higher precedence to multiplication than addition, in which case the correct interpretation of the example above is 1 + (2 * 3).
Note that Rule4 above is a semantic rule. It is possible to rewrite the grammar to incorporate this into the syntax. However, not all such rules can be translated into syntax.
Simple non-lookahead parser actions
Initially Input = [1, +, 2, *, 3]
Shift “1” onto stack from input (in anticipation of rule3). Input = [+, 2, *, 3] Stack = [1]
Reduces “1” to expression “E” based on rule3. Stack = [E]
Shift “+” onto stack from input (in anticipation of rule1). Input = [2, *, 3] Stack = [E, +]
Shift “2” onto stack from input (in anticipation of rule3). Input = [*, 3] Stack = [E, +, 2]
Reduce stack element “2” to Expression “E” based on rule3. Stack = [E, +, E]
Reduce stack items [E, +, E] and new input “E” to “E” based on rule1. Stack = [E]
Shift “*” onto stack from input (in anticipation of rule2). Input = [3] Stack = [E, *]
Shift “3” onto stack from input (in anticipation of rule3). Input = [] (empty) Stack = [E, *, 3]
Reduce stack element “3” to expression “E” based on rule3. Stack = [E, *, E]
Reduce stack items [E, *, E] and new input “E” to “E” based on rule2. Stack = [E]
The parse tree and resulting code from it is not correct according to language semantics.
To correctly parse without lookahead, there are three solutions:
The user has to enclose expressions within parentheses. This often is not a viable solution.
The parser needs to have more logic to backtrack and retry whenever a rule is violated or not complete. The similar method is followed in LL parsers.
Alternatively, the parser or grammar needs to have extra logic to delay reduction and reduce only when it is absolutely sure which rule to reduce first. This method is used in LR parsers. This correctly parses the expression but with many more states and increased stack depth.
Lookahead parser actions[clarification needed]
Shift 1 onto stack on input 1 in anticipation of rule3. It does not reduce immediately.
Reduce stack item 1 to simple Expression on input + based on rule3. The lookahead is +, so we are on path to E +, so we can reduce the stack to E.
Shift + onto stack on input + in anticipation of rule1.
Shift 2 onto stack on input 2 in anticipation of rule3.
Reduce stack item 2 to Expression on input * based on rule3. The lookahead * expects only E before it.
Now stack has E + E and still the input is *. It has two choices now, either to shift based on rule2 or reduction based on rule1. Since * has higher precedence than + based on rule4, we shift * onto stack in anticipation of rule2.
Shift 3 onto stack on input 3 in anticipation of rule3.
Reduce stack item 3 to Expression after seeing end of input based on rule3.
Reduce stack items E * E to E based on rule2.
Reduce stack items E + E to E based on rule1.
The parse tree generated is correct and simply more efficient[clarify][citation needed] than non-lookahead parsers. This is the strategy followed in LALR parsers.
See also[edit]
Backtracking
Chart parser
Compiler-compiler
Deterministic parsing
Generating strings
Grammar checker
LALR parser
Lexical analysis
Pratt parser
Shallow parsing
Left corner parser
Parsing expression grammar
DMS Software Reengineering Toolkit
Program transformation
Source code generation
References[edit]
^ a b “Parse”. Retrieved 27 November 2010.
^ Masaru Tomita (6 December 2012). Generalized LR Parsing. Springer Science & Business Media. ISBN 978-1-4615-4034-2.
^ “Grammar and Composition”.
^ Christopher D.. Manning; Christopher D. Manning; Hinrich Schütze (1999). Foundations of Statistical Natural Language Processing. MIT Press. ISBN 978-0-262-13360-9.
^ Jurafsky, Daniel (1996). “A Probabilistic Model of Lexical and Syntactic Access and Disambiguation”. Cognitive Science. 20 (2): 137–194. CiteSeerX 10. 1. 150. 5711. doi:10. 1207/s15516709cog2002_1.
^ Klein, Dan, and Christopher D. Manning. “Accurate unlexicalized parsing. ” Proceedings of the 41st Annual Meeting on Association for Computational Linguistics-Volume 1. Association for Computational Linguistics, 2003.
^ Charniak, Eugene. “A maximum-entropy-inspired parser. ” Proceedings of the 1st North American chapter of the Association for Computational Linguistics conference. Association for Computational Linguistics, 2000.
^ Chen, Danqi, and Christopher Manning. “A fast and accurate dependency parser using neural networks. ” Proceedings of the 2014 conference on empirical methods in natural language processing (EMNLP). 2014.
^ Jia, Robin; Liang, Percy (2016-06-11). “Data Recombination for Neural Semantic Parsing”. arXiv:1606. 03622 [].
^ Berant, Jonathan, and Percy Liang. “Semantic parsing via paraphrasing. ” Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers). 2014.
^ a b Aho, A. V., Sethi, R. and Ullman, J. (1986) ” Compilers: principles, techniques, and tools. ” Addison-Wesley Longman Publishing Co., Inc. Boston, MA, USA.
^ Sikkel, Klaas, 1954- (1997). Parsing schemata: a framework for specification and analysis of parsing algorithms. Berlin: Springer. ISBN 9783642605413. OCLC 606012644. CS1 maint: multiple names: authors list (link)
^ 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. (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.
^ Rekers, Jan, and Andy Schürr. “Defining and parsing visual languages with layered graph grammars. ” Journal of Visual Languages & Computing 8. 1 (1997): 27-55.
^ Rekers, Jan, and A. Schurr. “A graph grammar approach to graphical parsing. ” Visual Languages, Proceedings., 11th IEEE International Symposium on. IEEE, 1995.
^ Zhang, Da-Qian, Kang Zhang, and Jiannong Cao. “A context-sensitive graph grammar formalism for the specification of visual languages. ” The Computer Journal 44. 3 (2001): 186-200.
^ Jill Fain Lehman (6 December 2012). Adaptive Parsing: Self-Extending Natural Language Interfaces. ISBN 978-1-4615-3622-2.
^ taken from Brian W. Kernighan and Dennis M. Ritchie (Apr 1988). The C Programming Language. Prentice Hall Software Series (2nd ed. ). Englewood Cliffs/NJ: Prentice Hall. ISBN 0131103628. (Appendix A. 13 “Grammar”, p. 193 ff)
21. Free Parse HTML Codes [1]
Further reading[edit]
Chapman, Nigel P., LR Parsing: Theory and Practice, Cambridge University Press, 1987. ISBN 0-521-30413-X
Grune, Dick; Jacobs, Ceriel J. H., Parsing Techniques – A Practical Guide, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands. Originally published by Ellis Horwood, Chichester, England, 1990; ISBN 0-13-651431-6
External links[edit]
Look up parse or parsing in Wiktionary, the free dictionary.
The Lemon LALR Parser Generator
Stanford Parser The Stanford Parser
Turin University Parser Natural language parser for the Italian, open source, developed in Common Lisp by Leonardo Lesmo, University of Torino, Italy.
Short history of parser construction
Data Parser – What Is the Parsing of Data | By LimeProxies
In this part, we would be explaining the concepts and algorithms that are involved in data parsing, so that you can have a better understanding of what goes on. The three parts that would be dealt with here are;
Components and terms of a data parser
Grammars
Algorithms
1. COMPONENTS AND TERMS OF A DATA PARSER
A. REGULAR EXPRESSIONS
Regular expressions are a sequence of characters that have a pattern. Even though they are popularly regarded as not fit for parsing, they can be used for parsing simple input. The misconception is due to the errors that arise when regular expressions are used to parse everything including what they are not meant for. When that is done, it ends with a series of fragile regular expressions that ae hacked together.
Regular expressions can also be used to parse some simple programming languages. Not all languages can be parsed using regular expressions and the languages that can be used ae referred to as regular languages. Regular languages can also be parsed using a finite state machine and as this is also powerful, it can be used to implement lexers.
While you can define a regular language using a series of regular expressions, more complex languages require something more. As a rule, if a grammar of a language has elements that are either recursive or nested, it’s not a regular language. An instance is HTML. It can contain a random number of tags inside another tag and so it can’t be said to be a regular language. By extension, it cannot be parsed using only regular expressions no matter how skilled the parser is.
Regular Expressions in Grammars
Since most programmers are familiar with regular expressions, they are often used to define language grammar. Their syntax is more precisely used to define the rules of a parser or lexer. For instance, the Kleene star is applied in a rule as an indicator that a particular element can be present for any number of times starting from zero to infinity.
The rule is not the same as the implementation of a lexer or a parser. You can make use of the regular expression engine of your language to implement a lexer. For even better performance, the regular expressions in the grammar ae converted to a finite state machine.
B. STRUCTURE OF A-PARSER
A complete parser usually has two parts; the lexer which is also known as the scanner or tokenizer, and the proper parser. The parser doesn’t work directly on the text but only on the output of the lexer, and so it needs the lexer. Some parsers however do not have a separate lexer but rather combine the lexer and the parser. These are referred to as scanners parsers.
The lexer first scans the input and then produces the matching tokens after which the parser scans the tokens and gives the parsing result.
Scannerless Parsers
Scannerless parsers are different in the way they operate as they act directly on the original text instead of tokens produced by a lexer. So a scannerless parser acts both as a lexer and a parser.
It’s not important to define the grammar, but for purposes of debugging, you would need to know if a parser is scannerless or not.
C. GRAMMAR
Grammar rules that describe a language syntactically. A grammar describes a language and this is only applicable to the syntax and not the semantics. This implies that grammar defines the structure of language and not it’s meaning. The input must be checked by some other means to be sure it’s correct.
For instance, imagine a grammar for the language shown in the paragraph definition is to be defined;
HELLO: “Hello”
NAME: [a-zA-z] +
Greeting: HELLO NAME
The acceptable input by the grammar is “Hello Michael” and “Hello Programming”. Either way, they are correct. Since “Programming” isn’t a name however, it’s wrong semantically.
ANATOMY OF A GRAMMAR
There are some commonly used formats used to describe grammar and an example is the Backus-Naur Form (BNF). This format has variants, one of which is the Extended Backus-Naur Form and its advantage is its simplicity in denoting repetition. Another variant of BNF is the Augmented Backus-Naur Form. It’s useful in the description of bidirectional communications protocols. When using Backus-Naur grammar, a typical rule has the following representation;
The can be replaced by the group of elements on the right side; _expression_ and by this, is referred to as nonterminal. The other element _expression_ could also contain other nonterminal symbols as well as terminal ones.
Terminal symbols are the symbols that don’t appear as in the entirety of the grammar and an example is a string of characters like in “Three”.
A rule in the technical sense defines a transformation between the nonterminal set of elements and the nonterminal and terminal sets of elements on the right side. It is also known as production rule.
TYPES OF GRAMMARS
In parsing, two types of grammar exist basically. They are regular grammars and context-free grammars. Normally, regular grammar would be used to define a regular language and so on, but the recent kind of grammar known as Parsing Expression Grammar (PEG), can be used to also define context-free language since its as powerful as context-free grammars. The difference between the two types lies in the notation and the way the rules are interpreted.
In terms of complexity, regular languages are simpler than context-free languages and they can both be distinguished by the _expression_ of a regular grammar. This implies that the right side could be only one of the following;
A single terminal symbol
The empty string
A terminal symbol followed by a nonterminal one
This is easier in theory than in practice as it’s hard to check because a tool could allow the use of more terminal symbols in a single definition and then transform the expression in an appropriate series of expressions that al belong to one of the above-mentioned cases.
So even if you write an expression that isn’t compatible with a regular language, the expression would be transformed into the proper form.
D. LEXER
A lexer function to transform a sequence of characters present in a sequence of tokens thus they are also called scanners or tokenizers. Lexers are important in parsing as they transform the input into a form that is better manipulated by the parser at the later stage of the process. Normally, lexers are easier to write than parsers although in some cases, both are equally complex.
An important function of lexers is dealing with whitespace. You would need to discard the whitespace using lexer because its presence would have the parser checking for it between every token and it’s an annoying process. You can’t always discard the whitespace however as its relevance in some cases, for example in python where whitespace is used to identify a block of code. Even in such cases, the lexer is used to distinguish the relevant whitespace from the irrelevant ones before parsing.
WHERE THE FUNCTION OF LEXER ENDS AND THE PARSER BEGINS
In most cases, lexers ae used together with parsers so the division between the two can be difficult to make most times. This is because after parsing, the result should be one that is relevant to the program. So in the end you care only about the method of parsing that suits you even if there are many ways to parse data.
PARSER
In the broad sense, a data parser is a software that performs the entire process of parsing, but being specific, the parser analyses the tokens that are produced by the lexer. This implies that the parser handles the most important and difficult part of parsing and the lexer assists in the process.
The output of a parser is usually an organized structure of its code and it comes in the form of a tree. The tree could be a parse tree or an abstract syntax tree and the differences between each is in the way they represent the code and the intermediate elements defined by the parser. A tree is chosen because it makes it more convenient to work with the code at different levels.
Syntactic Correctness vs Semantic Correctness
Parsers are important in compilers or interpreters but are not restricted to these as they can also be a part of other software. A parser can be used to check the syntactic correctness of code, but in checking the semantic validity, the compiler would have to use the output.
In the following example, the code is syntactically correct, but incorrect semantically.
int x = 10
int sum = x + y
since the (y) variable is not defined, the program would fail if the code is executed. The parser wouldn’t know this as it only looks at the code’s structure rather than keep track of variables. A compiler on the other hand goes through the parse tree and keeps track of all the variables that are defined the first time. It goes over the tree a second time to cross-check that the variables that are used are properly defined.
SCANNERLESS PARSER
A scannerless parser is also called a lexerless parser and it performs tokenization and parsing all in one step. If the distinction between lexer and parser is not necessary, or difficult, it’s better to make use of a scannerless parser.
PROBLEMS WITH PARSING REAL PROGRAMMING LANGUAGES
Theoretically, parsing is meant to deal with real-life programming languages, but this is an issue due to some challenges.
Context-sensitive parts
Even though parsing tools are meant to handle context-free languages, the languages are context-sensitive in some cases and it becomes a problem. An example of a context-sensitive element is soft keywords (strings of elements that could act as keywords in certain places and also act as identifiers in others).
Whitespace
Whitespace is very important in some programming languages like python, where the indentation present on a statement indicates that it belongs to a certain block of code.
Even though whitespace is relevant in python, it is also irrelevant in some places such as the space between words or keywords. The problem is the indentation and the easiest way to deal with this is to check the indentation at the beginning of a line and transform it into the proper token.
Multiple Syntaxes
Another issue with using real programming languages in parsing in that the language might contain sections of code that have different syntaxes. The most common example of this is the C or C++ preprocessor which is a complicated language on its own it’s and can appear randomly inside any C code.
In the case of annotations, you can more easily deal with them, and they are present in many programming languages. They can be used to process the code before it gets to the compiler and can command the annotation processor to transform the code in a specific way before the annotated one. Since they only appear in specific places, they are easier to deal with.
Dangling Else
This problem is common in data parsing especially those that are linked to the if-then-else statement. Else clause is an optional one and so the if statement could mean anything. For example;
If one
Then if two
Then two
Else???
In the example, it isn’t clear if the else is for the first or second if.
In handling the problem, the method used conventionally involves associating the else to the nearest statement of if and doing this makes the parsing context-sensitive.
PARSE TREE AND ABSTRACT SYNTAX TREE
These two terms are closely related and sometimes used interchangeably. Both of them are similar as they are both trees and have a root with nodes that represents the entire source code. The root has subsequent nodes, which themselves contain subtrees that represent smaller portions of code until the emergence of single tokens.
The difference between the two is in their levels of abstraction. In a parse tree, you may find all the tokens that are in the program and also a set of intermediate rules. But in an abstract syntax tree, only the relevant information that helps to understand the code remains.
A parse tree represents the code that is closer to the concrete syntax. It shows different levels of details of the parsing process.
// lexer
PLUS: ‘+’
WORD PLUS: ‘plus’
NUMBER: [0-9]+
// parser
// the pipe | symbol indicates an alternative between the two
Sum: NUMBER (PLUS | WORD_PLUS) NUMBER
In the grammar, a sum can be determined with the plus (+) symbol or the use of the string plus.
When parsing the following code;
10 plus 21
The resulting parse and abstract syntax tree would be;
The indication of the specific operator is absent in the AST, and the only remaining in the operation yet to be performed. The specific operator is an intermediate rule.
GRAMMARS
Grammars are rules that are used to describe a language. Grammar has several elements that need to be given attention as grammar can also be used to define duties or to execute codes.
GRAMMAR ISSUES
Missing Token
In some types of grammar, only a few tokens are defined. Example;
NAME: [a-zA-Z]+
Greeting: “HELLO” NAME
The token “HELLO” isn’t defined and this usually happens because some tools generate the corresponding tokens for a string to save time.
Left-recursive Rules
An important feature of parsers is the support it should have for left-recursive rules. This implies that a rule should begin with a self-reference. This reference could be indirect and appear in another rule that is referenced by the first one.
For example, in arithmetic operations, an addition could be described as two expressions divided by the plus symbol, but the quantity of the additions could also be other additions.
Addition: expression ‘+’
expression
Multiplication: expression ‘+’
// an expression could be an addition or a multiplication or even a number expression: multiplication | addition | [0-9]+
In the above example, the expression has an indirect reference to itself through the rules of addition and multiplication.
The description is also similar to multiple additions like 5 + 4 + 3. It’s so because it can also be interpreted as expression (5) (‘+’) expression (4+3. The rule of addition here is that the first expression corresponds to the option [0-9]+ and the second one is also an addition. 4 + 3 can also be divided into its two constituent parts;
Expression (4) (‘+’)
Expression (3)
The rule of addition here is that both expressions correspond to the option [0-9]+
Since left-recursive rules may not be used with some parser generators, the other option would be a long chain of expressions that take care of the most important operations.
Predicates
Predicates are rules that match only under the required conditions. They are also called syntactic or semantic predicates. The required condition is defined using a code that is supported by the tool that the grammar was written for.
The advantage of predicates is that they permit some form of context-sensitive parsing which is unavoidable in matching certain elements sometimes. For example, they can be used to check if the sequence of characters that define a soft keyword is in the right position where it would ultimately be a keyword. Its disadvantage is that it can slow down the parsing process and also make grammar dependent on the programming language the condition is expressed in.
Embedded Actions
Embed actions single out codes that are executed once the rule is matched. Their disadvantage is that grammar is more difficult to read because the rules are surrounded by codes. Just like predicates, they also break the division between a language describing grammar, and the code that manipulates the parsing results.
Embedded actions are used more by less sophisticated parsing generators as the only way codes can easily get executed once a node is matched. With parser generators, the only way would be to access the tree and execute the right code yourself. With more advanced tools, you can execute arbitrary code using the visitor pattern when it’s needed.
Actions can also help to add certain tokens or change the generated tree and this could be the only option in dealing with complicated programming languages like C.
FORMATS
Concerning grammar, there are two main types of formats; BNF and all its variants, and PEG. Many tools also implement their specific variations of the formats, while some tools use custom formats completely. Custom format is made of three parts; options with the custom code, and then the lexer section which ends in the parser section.
Since BNF formats are the foundation of a context-free grammar, it could also be identified as CFG format.
BACKUS-NAUR FORM AND ITS VARIATIONS
The BNF is a very successful format and is the basis for the creation of PEG. Since its very simple, it’s not mostly used in its based form but rather in the form of a more powerful variant. In the below example, the importance of BNF variants can be seen;
The symbol can be transformed in any of the English letters and this example, only lowercase letters are valid. It is also applicable in which can be any of the alternative digits. The first issue with this is that you would have to list the alternatives individually and you can’t make use of character classes like with regular expressions.
A more difficult issue is that there is no simple way to denote optional elements or existing repetitions, so you would have to depend on Boolean logic and the (|) symbol.
From the rule, can be made of a character, or a shorter that comes before a
The below example would be the tree parse for “dog”
Other limitations of BNF are that it makes it difficult to make use of empty strings or symbols that are used by the format in the grammar.
Extended Backus-Naur Form
The EBNF was created to solve some of the above-mentioned limitations. It’s the most popular form used in parsing tools and even though tools may differ from the standard notation. EBNF’s notation is cleaner and adopts more operators to deal with optional elements or concatenation.
ABNF
ABNF is short for Augmented BNF and is one of the variants of BNF. It’s developed for the main purpose of describing bidirectional communication protocols. The use of ABNF can be as productive as that of EBNF, but due to some of its features, its use is limited to internet protocols.
ABNF also has a different syntax from that of EBNF. For example, the alternative operator is represented with the slash (/). It also has more features than EBNF, for instance, you can define numeric ranges like%x30-39 as [0-9]. It’s also used by designers to include standard character classes-like rules that the final user can use.
PEG
PEG is short for Parsing Expression Grammar. It’s a format that stems from an old grammar format called Top-Down Parsing Language. It’s similar to EBNF and is also used to support widely used variables like character ranges. It’s not entirely similar to EBNF like its use of the formal arrow symbol instead of the normal equals symbol in assignments.
PEG vs CFG
Theoretically, the differences between both formats are limited. PEG is closely likened to the packrat algorithm and that’s it. For example, PEG does not allow left recursion but although the algorithm can be modified to support left recursion, it eliminates the linear time parsing property. PEG parsers are also generally scannerless parsers.
A difference and probably the most important between PEG and CFG is that when ordering choices in PEG, its meaningful unlike in CFG. If there are various valid ways of parsing an input, it will be ambiguous in CFG and an error will return. For example, by providing all the valid results to the developer for sorting out. In PEG, however, ambiguity is eliminated because the first applicable choice will be chosen and so PEG can’t be ambiguous.
The disadvantage is that you have to be extra careful when listing possible alternatives because you would have unexpected consequences in the long run.
ALGORITHMS
Parsing has different algorithms that each have their strong points as well as their weak points, and require updates frequently.
Parsing has two strategies which are; top-down parsing, and bottom-up parsing. Both are defined using the parse tree as generated by the parser.
A top-down parser functions to identify the root of the parse tree first and then goes to the subtrees and then the leaves of the tree. While a bottom-up parser begins from the bottom of the tree and works its way up till the root of the tree.
Initially, it was easier to build top-down parsers even though bottom-top parsers proved to be more powerful. But due to advancement in tech, the situation is now more balanced.
The derivation is related to the strategies and it indicates the order of appearance in which nonterminal elements in the rule on the right are applied to get the nonterminal symbol on the left side. With BNF terms, it can be said to indicate how the elements in –expression_ are used to get. The two possibilities that exist are leftmost derivation and rightmost derivation. The first one is an indication of the rules that are applied from left to right, and the second indicates rules that are applied from right to left.
For example, in trying to parse the symbol result as defined in the following grammar;
expr_one =.. // stuff
expr_two =.. // stuff
result = expr_one ‘operator’ expr_two
you can choose to apply the rule for symbol expr_one before expr_two or the other way round. For leftmost derivation, you choose the first option, but you pick the second option for rightmost derivation.
When applying derivation, it’s depth-first or recursively. This means that its first applied to the first expression and then on the intermediate result that is obtained.
COMMON ELEMENTS
These common elements are shared between parsers that are built using top-down and bottom-up strategies.
Lookahead and Backtracking
Lookahead is used to indicate the number of elements that come after the current one and ae taken into consideration for decision making. For example, a parser might check the token that comes next to help in deciding the rule to apply now. After the right rule is matched, the token is consumed but the next one stays in the queue.
Backtracking on the other hand is a technique specific to an algorithm that finds solutions to complex problems by trying out partial solutions and dwelling on the most promising one. If the solution that is being tested fails, the parser then backtracks and tries out another one.
Chart Parsers
Chart parsers could be wither bottom-up, or top-down. They try to avoid backtracking with the use of dynamic programming. Dynamic programming is a method that is used to break down larger problems into smaller ones for easy solving.
The Viterbi algorithm is an example of a common dynamic programming algorithm that chart parser uses. It aims to locate the most likely hidden states through the known sequence of events.
AUTOMATONS
Automatons are abstract machines. Among parsers, Pushdown Automaton (PDA) is common, and among lexers, Deterministic Finite Automation (DFA) is common. PDA is a more powerful and complex machine than DFA.
Since they are used to define abstract machines, they are not directly linked to a real algorithm but are rather used to give a formal description of the level of complexity an algorithm has to be able to deal with.
Since DFA are state machines, the distinction when it comes to lexer is frequently uncertain. This is so because state machines have ready to use libraries and so DFA is implemented most times with a state machine.
Lexing With a Deterministic Finite Automaton
A state machine has many possible states, each with a transition function and an example of a finite-state machine is DFA. The transition functions are responsible for how the machine moves from one state to another in a response to an event. If the machine is used for lexing, the input characters are fed in one at a time until it can build a token.
They are used because they can recognize an exact set of regular languages and so they are as powerful as regular languages. Another reason is that they are a few mathematical methods that can be used to check their properties and manipulate them, and they can work with an online algorithm.
An online algorithm doesn’t require the whole input to work. With a lexer, a token can be recognized as soon as its characters reach the algorithm. You can also transform a set of regular expressions in a DFA and this makes it easy to input the rules in a simple enough way so that developers won’t have any problem working with it. From there you can convert them automatically in a state machine that can work on them efficiently.
TOP-DOWN ALGORITHMS
This is the most popular strategy of the two, and it’s applied in several algorithms.
LL PARSER
The LL stands for left-to-right read of the input, leftmost derivation. These parsers are table based and don’t have backtracking, only lookahead. From this, you can see that they don’t depend on any table to make decisions on parsing rules to apply. They find the correct rules to apply by;
The parser first looks at the current token and also the required amount of lookahead tokens
And then it applied the different rules until the right match is found
LL parsers are not specific to a specific algorithm but a class of parsers. So an LL parser can parse LL grammar. LL grammars are defined by the number of lookahead tokens that are required to parse them and the number is indicated between parenthesis next to LL; LL(k).
So it’s safe to say that an LL(k) parser makes use of k tokens of lookahead and so it can parse a grammar that requires k tokens of lookahead to be parsed. LL(k) grammars are used when different algorithms are being compared and it serves as a meter.
Value of LL Grammars
The use of LL grammars from above is because LL parsers are a bit restrictive, and they both have wide use. LL grammars don’t support left-recursive rules and so you can transform any left-recursive grammar and this limitation has an effect on productivity and power.
The loss of productivity is based on the requirement that you have to write the grammar in a specific way and this is time-consuming. Power is limited also because a grammar that may need 1 token of lookahead usually when written using a left-recursive tool may need 2 to 3 tokens of lookahead when it gets written in a non-recursive way.
Loss of productivity can be reduced using an algorithm that transforms a left recursive grammar to a non-recursive one. An example of a tool that can do that is ANTLR but if you are building your own data parser, you would have to do it yourself.
LL(1) and LL(*) are two special types of LL(k) grammars and were the only practical types in the past due to the ease at which parsers could be built for them.
EARLEY PARSER
The Earley parser is a chart kind of parser. This algorithm is likened to CYK, another similar parser but simpler but worse in memory and performance too. An advantage of the Earley algorithm over CYK is that after storing partial results, it also has a feature to predict the rule that is going to be matched next.
Earley parser works basically by dividing a rule into parts. An example is seen below;
// an example grammar
HELLO: “hello”
Greeting HELLO NAME
// Earley parser would break up greeting in the following way
//. HELLO NAME
// HELLO. NAME
// HELLO NAME.
An upside of Earley parser is the guarantee that it can parse all context-free languages, whereas other algorithms like LL or LR can only parse a subset of the languages. For instance, it has no issue with left-recursive grammars. In the general sense, Earley parser is also able to handle non-deterministic and ambiguous grammars.
It can do all of these but at the risk of a bad performance. It however has a linear time performance for normal grammars. The good thing though is that the languages that are parsed by the more traditional algorithms are usually the one of interest.
A side effect to this is that it has no limitations as it forces the developer to write the grammar following a certain format that the parsing can be more efficient. That is to say, building a LL(1) grammar might prove to be difficult for the developer, but the parser on the other hand can apply it better. So Earley makes you work less as the parser does the rest.
In a simple statement, you can say that Earley allows you to make use of grammar that is easier to write even though the performance may not be optimal.
Earley Parser Use Cases
Earley parsers as we have seen are easy to use, but in terms of performance, they are lacking. This up and downside make the algorithm more suitable for use in an educational setting where productivity is more important than speed.
In the first use case, the grammars your user writes work properly but the parser sends random errors at intervals. The errors are actually because of limitations that exist in the algorithm that your users do not understand. So by getting the errors your users are being forced to understand the working of your parser and its unnecessary.
A good case of a situation where the productivity of a parser is more important than its speed is in the use of a parser generator to implement syntax highlighting to help an editor. The editor needs support for many languages and being able to give support quickly might be more important than completing the task quicker.
PACKRAT (PEG)
Packrat and PEG were both invented by the same person and so they are often associated with one another. Packrat parsing has a linear execution time and this is because there is no backtracking. Another reason for its good efficiency is memorization. This is the process of storing partial results while parsing is going on. A drawback however is the amount of memory space that is needed to store the results during the parsing process. If the available memory is not up to that which is required, the linear execution time of the algorithm is lost.
Packrat just as others don’t support left-recursive rules and that is because PEG needs to always choose the first option. Some variants of the algorithm can support direct left-recursive rules but they do this at the price of losing linear complexity.
If necessary, packrat can also perform with an infinite amount of lookahead and this goes on to influence the execution time.
RECURSIVE DESCENT PARSER
This type of parser works with a set of recursive procedures and most times each procedure is to a rule of grammar. And so you can say that the parser structure is a mirror of grammar structure.
A predictive parser is sometimes used as a synonym for a top-down parser, while some others use it to mean a recursive descent parser without backtracks. A recursive descent parser that backtracks is the direct opposite of the second meaning of a predictive parser. So with a backtracking recursive descent parser, whenever a rule in a sequence fails to match the input, it goes back to try another.
Recursive descent parsers do not easily parse left-recursive rules. This is so because the algorithm would repeatedly call the same function over and over. To solve this, you can use tail recursion, and parsers that use this method to solve the repeated calling of a function are called tail-recursive parsers.
Tail recursive parsers are recursions at the end of a function. They are however not used alone but together with transformations of grammar rules and this combination allows recursive-descent parsers to deal with left-recursive rules.
PRATT PARSER
Even though Pratt Parsers are not widely used, those that know its value appreciate it. This algorithm doesn’t rely on grammar but instead on tokens.
Conventionally, top-down parsers work better if there is a prefix that distinguishes different rules. Since this applies to all programming languages, it’s one reason why the Pratt Parser has just a little effect in the world of data parsing.
Pratt algorithm however has great use in expressions. Because of precedence, it’s impossible to understand the input structure just by looking at the order of the tokens. So the algorithm asks for an allocation of precedence value to each token and other functions too that determine actions based on what is to the left and the right side of the token.
PARSER COMBINATOR
This is a higher-order function and it works by accepting parser function as the input and sends the new parser function as the output. A pars
Frequently Asked Questions about parser data
What does a parser do?
A parser is a software component that takes input data (frequently text) and builds a data structure – often some kind of parse tree, abstract syntax tree or other hierarchical structure, giving a structural representation of the input while checking for correct syntax.
What is parsing in data mining?
Data parsing is the next step done immediately after the extraction of data. It is the process of converting received data into a different format that is more acceptable and readable.Jul 20, 2020
What is data parsing and extraction?
DATA-EXTRACTION. Data parsing is used for crawling information from large datasets and structuring it in a way humans can understand. Traditional data parsing is done on HTML files where the parser converts HTML text into readable data.Sep 1, 2021
