Data Parsing Meaning

What is data parsing? – ScrapingBee

07 June, 2021
10 min read
Kevin worked in the web scraping industry for 10 years before co-founding ScrapingBee. He is also the author of the Java Web Scraping Handbook.
Data parsing is the process of taking data in one format and transforming it to another format. You’ll find parsers used everywhere. They are commonly used in compilers when we need to parse computer code and generate machine code.
This happens all the time when developers write code that gets run on hardware. Parsers are also present in SQL engines. SQL engines parse a SQL query, execute it, and return the results.
In the case of web scraping, this usually happens after data has been extracted from a web page via web scraping. Once you’ve scraped data from the web, the next step is making it more readable and better for analysis so that your team can use the results effectively.
A good data parser isn’t constrained to particular formats. You should be able to input any data type and output a different data type. This could mean transforming raw HTML into a JSON object or they might take data scraped from JavaScript rendered pages and change that into a comprehensive CSV file.
Parsers are heavily used in web scraping because the raw HTML we receive isn’t easy to make sense of. We need the data changed into a format that’s interpretable by a person. That might mean generating reports from HTML strings or creating tables to show the most relevant information.
Even though there are multiple uses for parsers, the focus of this blog post will be about data parsing for web scraping because it’s an online activity that thousands of people handle every day.
How to build a data parser
Regardless of what type of data parser you choose, a good parser will figure out what information from an HTML string is useful and based on pre-defined rules. There are usually two steps to the parsing process, lexical analysis and syntactic analysis.
Lexical analysis is the first step in data parsing. It basically creates tokens from a sequence of characters that come into the parser as a string of unstructured data, like HTML. The parser makes the tokens by using lexical units like keywords and delimiters. It also ignores irrelevant information like whitespaces and comments.
After the parser has separated the data between lexical units and the irrelevant information, it discards all of the irrelevant information and passes the relevant information to the next step.
The next part of the data parsing process is syntactic analysis. This is where parse tree building happens. The parser takes the relevant tokens from the lexical analysis step and arranges them into a tree. Any further irrelevant tokens, like semicolons and curly braces, are added to the nesting structure of the tree.
Once the parse tree is finished, then you’re left with relevant information in a structured format that can be saved in any file type. There are several different ways to build a data parser, from creating one programmatically to using existing tools. It depends on your business needs, how much time you have, what your budget is, and a few other factors.
To get started, let’s take a look at HTML parsing libraries.
HTML parsing libraries
HTML parsing libraries are great for adding automation to your web scraping flow. You can connect many of these libraries to your web scraper via API calls and parse data as you receive it.
Here are a few popular HTML parsing libraries:
Scrapy or BeautifulSoup
These are libraries written in Python. BeautifulSoup is a Python library for pulling data out of HTML and XML files. Scrapy is a data parser that can also be used for web scraping. When it comes to web scraping with Python, there are a lot of options available and it depends on how hands-on you want to be.
If you’re used to working with Javascript, Cheerio is a good option. It parses markup and provides an API for manipulating the resulting data structure. You could also use Puppeteer. This can be used to generate screenshots and PDFs of specific pages that can be saved and further parsed with other tools. There are many other JavaScript-based web scrapers and web parsers.
For those that work primarily with Java, there are options for you as well. JSoup is one option. It allows you to work with real-world HTML through its API for fetching URLs and extracting and manipulating data. It acts as both a web scraper and a web parser. It can be challenging to find other Java options that are open-source, but it’s definitely worth a look.
There’s an option for Ruby as well. Take a look at Nokogiri. It allows you to work with HTML and HTML with Ruby. It has an API similar to the other packages in other languages that lets you query the data you’ve retrieved from web scraping. It adds an extra layer of security because it treats all documents as untrusted by default. Data parsing in Ruby can be tricky as it can be harder to find gems you can work with.
Regular expression
Now that you have an idea of what libraries are available for your web scraping and data parsing needs, let’s address a common issue with HTML parsing, regular expressions. Sometimes data isn’t well-formatted inside of an HTML tag and we need to use regular expressions to extract the data we need.
You can build regular expressions to get exactly what you need from difficult data. Tools like regex101 can be an easy way to test out whether you’re targeting the correct data or not. For example, you might want to get your data specifically from all of the paragraph tags on a web page. That regular expression might look something like this:

(. *)<\/p>/
The syntax for regular expressions changes slightly depending on which programming language you’re working with. Most of the time, if you’re working with one of the libraries we listed above or something similar, you won’t have to worry about generating regular expressions.
If you aren’t interested in using one of those libraries, you might consider building your own parser. This can be challenging, but potentially worth the effort if you’re working with extremely complex data structures.
Building your own parser
When you need full control over how your data is parsed, building your own tool can be a powerful option. Here are a few things to consider before building your own parser.
A custom parser can be written in any programming language you like. You can make it compatible with other tools you’re using, like a web crawler or web scraper, without worrying about integration issues.
In some cases, it might be cost-effective to build your own tool. If you already have a team of developers in-house, it might not too big of a task for them to accomplish.
You have granular control over everything. If you want to target specific tags or keywords, you can do that. Any time you have an update to your strategy, you won’t have many problems with updating your data parser.
Although on the other hand, there are a few challenges that come with building your own parser.
The HTML of pages is constantly changing. This could become a maintenance issue for your developers. Unless you foresee your parsing tool becoming of huge importance to your business, taking that time from product development might not be effective.
It can be costly to build and maintain your own data parser. If you don’t have a developer team, contracting the work is an option but that could lead to step bills based on developers’ hourly rates. There’s also the cost of ramping up developers that are new to the project as they figure out how things work.
You will also need to buy, build, and maintain a server to host your custom parser on. It has to be fast enough to handle all of the data that you send through it or else you might run into issues with parsing data consistently. You’ll also have to make sure that server stays secure since you might be parsing sensitive data.
Having this level of control can be nice if data parsing is a big part of your business, otherwise, it could add more complexity than is necessary. There are plenty of reasons for wanting a custom parser, just make sure that it’s worth the investment over using an existing tool.
Parsing meta data
There’s also another way to parse web data through a website’s schema. Web schema standards are managed by, a community that promotes schema for structured data on the web. Web schema is used to help search engines understand information on web pages and provide better results.
There are many practical reasons people want to parse schema metadata. For example, companies might want to parse schema for an e-commerce product to find updated prices or descriptions. Journalists could parse certain web pages to get information for their news articles. There are also website that might aggregate data like recipes, how-to guides, and technical articles.
Schema comes in different formats. You’ll hear about JSON-LD, RDFa, and Microdata schema. These are the formats you’ll likely be parsing.
JSON-LD is JavaScript Object Notation for Linked Data. This is made of multi-dimensional arrays. It’s implemented using the standards in terms of SEO. JSON-LD is generally more simple to implement because you can paste the markup directly in an HTML document.
RDFa (Resource Description Framework in Attributes) is recommended by the World Wide Web Consortium (W3C). It’s used to embed RDF statements in XML and HTML. One big difference between this and the other schema types is that RDFa only defines the metasyntax for semantic tagging.
Microdata is a WHATWG HTML specification that’s used to nest metadata inside existing content on web pages. Microdata standards allow developers to design a custom vocabulary or use others like
All of these schema types are easily parsable with a number of tools across different languages. There’s a library from ScrapingHub, another from RDFLib.
We’ve covered a number of existing tools, but there are other great services available. For example, the ScrapingBee Google Search API. This tool allows you to scrape search results in real-time without worrying about server uptime or code maintainance. You only need an API key and a search query to start scraping and parsing web data.
There are many other web scraping tools, like JSoup, Puppeteer, Cheerio, or BeautifulSoup.
A few benefits of purchasing a web parser include:
Using an existing tool is low maintenance.
You don’t have to invest a lot of time with development and configurations.
You’ll have access to support that’s trained specifically to use and troubleshoot that particular tool.
Some of the downsides of purchasing a web parser include:
You won’t have granular control over everything the way your parser handles data. Although you will have some options to choose from.
It could be an expensive upfront cost.
Handling server issues will not be something you need to worry about.
Final thoughts
Parsing data is a common task handling everything from market research to gathering data for machine learning processes. Once you’ve collected your data using a mixture of web crawling and web scraping, it will likely be in an unstructured format. This makes it hard to get insightful meaning from it.
Using a parser will help you transform this data into any format you want whether it’s JSON or CSV or any data store. You could build your own parser to morph the data into a highly specified format or you could use an existing tool to get your data quickly. Choose the option that will benefit your business the most.
Data Parser - What Is the Parsing of Data | By LimeProxies

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
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.
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.
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.
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;
::= _expression_
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.
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.
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.
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.
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.
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.
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 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
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.
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
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 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.
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 ‘+’
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 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.
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.
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;
::= “a” | “b” | “c” | “d” | “e” | “f” | “g” | “h” | “i” | “j” | “k” | “l” | “m” | “n” | “o” | “p” | “q” | “r” | “s” | “t” | “u” | “v” | “w” | “x” | “y” | “z”
::= “0” | “1” | “2” | “3” | “4” | “5” | “6” | “7” | “8” | “9”
::= |
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 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 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.
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.
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.
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 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.
This is the most popular strategy of the two, and it’s applied in several algorithms.
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.
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”
// Earley parser would break up greeting in the following way
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 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.
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.
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.
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
What is data parsing? - Definition of data parser - Docsumo

What is data parsing? – Definition of data parser – Docsumo

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. However, not all parsers work the same and there are distinct differences in parsing technologies. There are numerous benefits of data parsing for businesses ranging from automated data extraction, improved visibility, cutting costs, and boosting employee productivity. But parsing doesn’t stop there, and today we’ll dive into what it is all is Data Parsing? Data parsing is a process in which a string of data is converted from one format to another. If you are reading data in raw HTML, a data parser will help you convert it into a more readable format such as plain text. Not all the information is converted during the parsing process and programs have their own sets of rules when it comes to parsing short, a data parse program is used for converting unstructured data into JSON, CSV, and other file formats and adds structure to said rsing DefinitionIn the field of computer programming, the definition of parsing is to analyze a string of symbols, special characters, and data structures using Natural Language Processing (NLP). When you define extracting in parsing, it refers to structuring information from data sets and giving it meaning by organizing it, based on user-defined rsing has different definitions for linguists and computer programmers but the general consensus is that it is used for analyzing sentences and mapping semantic relationships between them. In other words, you define extracting information from files and filtering through them as of Data ParsingData parsing takes two approaches when it comes to the semantic analysis of text- grammar-driven data parsing and data-driven data parsing. An important aspect of parsing is to capture information from data in a way that it fits contextual is how these two approaches work:-1. Grammar driven data parsingGrammar driven data parsing means the parser uses a set of formal grammar rules for the parsing process. The way this works is sentences from unstructured data get fragmented and transformed into a structured format. The problem with grammar-driven data parsing is that models lack robustness. This is overcome by relaxing the grammatical constraints so that sentences outside the scope of grammar rules can be ruled out for later analysis. Text parsing is a subset of grammar parsing and assigns a number of analyses to a given string. It resolves disambiguation problems faced by traditional methods of parsing as well. 2. Data-driven data parsingData-driven data parsing uses a probabilistic model and bypasses deductive approaches of text analysis often used by grammar-driven models. In this type of parsing, the parsing program applies rule-based techniques, semantic equations, and Natural Language Processing (NLP) for sentence structuring and analysis. Unlike grammar-based parsing, data-driven data parsing employs statistical parsers and modern treebanks for obtaining broad coverage from languages. Parsing conversational languages and sentences that require precision with domain-specific unlabelled data fall under the scope of data-driven data parser use casesWhat does a parser do? It extracts data from documents, gives structure to it, and filters parsing is used by different industry verticals to convert information into electronic formats from documents. The following are the most popular use-cases of parsing in industries:1. Business workflow optimizationData parsers are used by companies to structure unstructured datasets into usable information. Businesses use data parsing for optimizing their workflows related to data extraction. Parsing is used in the fields of investment analysis, marketing, social media management, and other business applications. Finance and AccountingBanks and NBFCs use data parsing to scrape through billions of customer data and extract key information from applications. Data parsing is used for analyzing credit reports, investment portfolios, income verification, and deriving better insights about customers. Finance firms use parsing for determining interest rates and loan repayment periods post-data extraction. 3. Shipping and LogisticsBusinesses that deliver products/services online use data parsers to extract billing and shipping details. Parsers are used for arranging shipping labels and ensuring the formatting of data is correct. 4. Real estate industryLead data is extracted from real estate emails by property owners and builders. Parsing technologies are used for extracting data for CRM platforms and process documentation in order to forward to real estate agents. From contact details, property addresses, cash flow data, and lead sources, parsers are very beneficial for real estate companies when it comes to making purchases, rentals, and you build your own Parser? A common question that keeps cropping up when document processing in organizations is whether or not you should build your own data parser. Custom text parsing software built for in-house teams is definitely tailor-made to meet specific parsing requirements within ever, the downside is that the whole staff has to be trained on how to use it. The costs of building a custom parse program can be steep since more time and resources are needed. Additionally, these solutions require a lot of planning and need their own dedicated servers for faster parsing. If you’re migrating systems, they may not be compatible with new technologies and will require ideal scenario is to use a data parser that is compatible with legacy systems and designed for various use-cases. Docsumo’s data parser gives you complete control of your data extraction and is designed to work with all types of businesses, be it startups, enterprises, or large-scale nclusionData parsing makes information accessible for organizations and allows it to be read more easily. The converted data can be shared across clients efficiently and parsers are designed to make business operations agile and scalable by nature. With a good parser, much of the manual work involved in data extraction and cleanup gets automated and its importance cannot be understated.
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Frequently Asked Questions about data parsing meaning

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

What is parsing in simple terms?

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).

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