What Does It Mean To Parse A File
What is Parse? – Computer Hope
Updated: 12/29/2017 by
To parse data or information means to break it down into component parts so that its syntax can be analyzed, categorized, and understood.
If an error occurs while parsing information a parse error is generated. A parse error may happen for any of the following reasons.
Reasons why a parse error may happen
The file containing the data to be parsed does not exist.
The data to be parsed contains an error. If you downloaded the file causing the parse error, try downloading the file again or look for an updated version of the file. If possible, try downloading the file from a different site.
You may have insufficient permissions to access the file’s data.
The file’s data is not compatible with the version of your operating system or program.
Insufficient disk space. If a file is written to a drive (e. g., thumb drive or SD card) that doesn’t have enough space for the parsed results, an error is generated. Make sure the drive has enough space or move the file being parsed to your hard drive if it’s being run from removable media.
Parse error with Excel or another spreadsheet formula
A parse error can also be encountered with a spreadsheet formula if the formula is not formatted correctly. Formula parse errors may happen when extraneous special characters are included in the formula, such as an extra quote. In general, any syntax error in the formula causes a parse error.
Error, Programming terms, Strip
Parse information and data from files with Python, Java, Ruby …
Looking to parse and extract information from files? Head over to Nanonets to automate the process of parsing, extracting, exporting and organizing information from your files! IntroductionWe live in a digital world, generating unlimited amounts of data every day. Many users have access to this raw data in different forms such as documents, images, videos, HTML pages, etc. The ability to extract essential information from this data will decide whether we can generate a compelling advantage for our business with this ever, automating the information extraction process is challenging when dealing with a vast amount of data. Furthermore, as the data accumulation increases, so does the need to read and understand it. This is where data parsers come into the picture. In simple terms, parsing data can help turn unstructured data or unreadable data into more readable and valuable ‘s take a deep dive into parsing different data types using various tools and techniques (especially with OCR). Additionally, we’ll be discussing a few data parsing business use-cases that can help automate and create workflows on top of your existing data. Below are the topics we’ll be covering in this article:
Introduction
How to Parse Information from Files?
The Use of OCR in Parsing Information from Files
Parsing Files Using Different Programming Languages
Automating the Process of Parsing Info or Data from Files
Example Use Cases of Parsing Data from Files
Workflows and Integrations with File Parsing
Nanonets Advantage in Parsing Info from Files
Let’s get started! Looking to parse and extract information from files? Head over to Nanonets to automate the process of parsing, extracting, exporting and organizing information from your files! How to Parse Information from Files? Before we answer how to parse information from files, let’s first learn the Data Parser fundamentals. A data parser helps us turn unstructured data/un-readable data into structured data. Data Parsers are also used to convert data from one type to example, let’s assume we have an HTML file, and we would need to convert it into a PDF file. In this case, we could use a data parser that parses (reads) through the HTML file, extracts the necessary information and exports it to the PDF file. Similarly, there are several problems that a data parser could solve when dealing with massive amounts of parsers are usually programmatically built based on particular data to read, analyse, transform, and provide more structured results. Now, with lots of open-source technologies and several active programming communities, we could find several data parsing tools online that can automate several business use-cases for free. However, when the data is complex, we might need to write some additional rules and conditions, which are sometimes OCR based parser to Convert PDF to JSON DataNow, let’s look at some of the essential concepts needed for parsing information from mponents of a Parser: Components are different types of syntax we find in data. For example, these can be unique characters, white space, regular expressions and many more. Therefore, when building a parser, we need to define all the rules to identify all these types of ammar: The structure of data might not be the same for all the data. Therefore, for a parser, we define grammar which are rules used to describe a language. It has several elements that can identify expressions, missing tokens gorithms: Parsers have different algorithms that each have their strengths and weaknesses. Usually, there are two strategies; 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 tree’s leaves. In comparison, a bottom-up parser begins from the bottom of the tree and works its way up until the tree’s root. This is how usually we parse through different kinds of are some of the common examples of how parsers can help extract data or convert data:Convert HTML data into readable dataExport data from PDF files to JSONParsing through email data to extract meaningful informationExtract data from images or scanned dataGet essential data from complex, nested JSONKey-value pair extraction from documentsHTML to Text with Parse Tree (Source)In the next section, let’s look at parsing data from image files or scanned Use of OCR in Parsing Information from FilesWe often see lots of data in the form of images or scanned copies. For example, documents related to financing, corporate businesses, manufacturing industries contain lots of data that cannot be edited directly. Some sectors have started using this modern tech to extract data from images. However, many still use data-entry operators to manually store and verify all document-based information which is time-consuming and error-prone. OCR (Optical Character Recognition) based parsers could be used to help automate this manual work of extracting data from scanned files. As abbreviated, the goal of OCRs is to recognise characters, basically text from the images, by performing several mathematical rsing through Tables with OCRNow, let’s consider a real-world example:Let’s say we need to save and analyse all the scanned receipts collected at a particular store. Usually, without OCRs, the only option is to enter all the data manually, but with OCR, we could pull out all the text from the receipt. However, the data from the OCR is not straightforward. It has much unprocessed and unrelated information such as text coordinates, special characters and unwanted spaces. This is where parsers can help read the data and make them more structured. Below is a screenshot of one of the workflows in which users’ OCRs and parsers extract meaningful rsing through Receipts (Reference) In the above image, first, a receipt is digitised using an OCR. The output from the information is usually in a key-value pair, which isn’t great to read with lots of information. These key-value formats are broken down into strings with simple serialisation techniques. Next, Natural Language Processing (NLP) based tagging algorithms such as Named-Entity-Recognition (NER) is utilised to identify the necessary tags; for example, these can be receipt id, receipt items, total, taxes etc. Lastly, we bring in the parser to convert the BIO tags into readable key-value is how a typical workflow looks for simple data. We could utilise popular OCR tools like Tesseract or techniques like zonal OCR, which is free and open-source. However, when the data is not generic, or say if we have invoices with multiple templates, we might need a powerful OCR that can handle fonts, text alignments, noisy images, tables, and many more. For more information about this, read rsing Files Using Different Programming LanguagesSo far, we’ve learned what a data parser does and looked at some use-cases. This section will look at how we can utilise different programming languages and frameworks to build them. Firstly, before choosing a programming language or a tool, we’ll need to review our data. If we’re working on more analytical data that needs deep-parsing and lots of cleaning, Python is a good choice as there are many data-analysing and parsing libraries. But, when working on giant datasets, Python might not be a great fit if we search through terabytes of data, as it’s a bit slow in execution. In such cases, languages like C and C++ are more valuable. Now, let’s dive into each of these programming languages and learn about their advantages and Python has been one of the most loved for tasks like working on data. It’s simple, fast, and easily deployable. There are several libraries in Python that can be utilised to build a data parser. Some popular libraries are Pandas, Numpy, and Scikit-learn. However, these libraries are widely used for performing type-conversion operations on massive datasets. For example, Pandas helps us convert complex tables, SQL files, Series into simple data frames. We can also export these data frames into any form, say SQL tables, HTML files or PDFs, with more insights from the ’s say, if our data is of JSON format, and we need to export it by building a parser into an excel sheet, we could directly import the internal JSON package. It’s convenient and straightforward to use, not just in dev environments; this is used across web apps for exporting data into JSON via database tables. Below is a simple example:If you have a list of items and want to export it as JSON, you could use the following in Python:my_list = [“Author: Jones”, “Age: 29”, “Topics: Machine Learning and AI”]
d= dict()
for i in my_list:
k, v = (‘:’)[0], (‘:’)[1]
d[k] = v
(d)Output:'{“Author”: ” Jones”, “Age”: ” 29″, “Topics”: ” Machine Learning and AI”}’Here, we wrote a simple for loop and iterated through the for loop; we split the string based on a special character (“:”) and added it into a dictionary. Next, we used method to convert the python dictionary into a complete JSON ’s one more example on converting lines in a text file into items in an array:lines = []
with open(“”) as file_in:
for line in file_in:
(line)Python has an in-built library for extracting patterns using regular expressions. Using this, we could build parsers that can be used on vast text data or any unstructured data. In documents, fields like dates, emails, pricing can be easily pulled ’s this simple. However, doing the same in programming languages like C or C++ might be more rsing with OCR in Python: This is a bonus section, only in Python language, as it comes with many powerful tools for working on Images. Therefore, when we’re working on image data sets and building workflows to extract data with parsers, we could utilise modules like open-cv, sci-kit-learn, and pillow to find and extract necessary text. On top of these, as discussed, we could use OCR engines like a tesseract. Also, building this for simple formatted data doesn’t consume much time; all these are easy to install and can be deployed into production within Process Flow with Python and Tesseract (Source)But when the images are not generic or complex regarding their layouts and text positions, we’ll have to use deep learning frameworks like Tensorflow and PyTorch to build custom Deep Learning algorithms for key-value pair extraction, BIO Tagging, and NER. If you’re not experienced with these, don’t worry, you could always use tools like Nanonets or other third party Parsing with Java: Java is one of the most influential and robust programming languages. To date, a lot of top businesses use Java for their web applications and mobile apps. One advantage is that Java comes with a powerful scanner class that can parse through different files and does operations like search and extract. However, comparatively, the syntax might not be as handy as Python, but it still gets the job done much faster. Here’s an example of how we can use the Scanner class and parse through files in Java:public class ReadFile {
public static void main(String[] args) throws IOException {
String token1 = “”;
Scanner inFile1 = new Scanner(new File(“”)). useDelimiter(“, \\s*”);
List
while (inFile1. hasNext()) {
// find next line
token1 = ();
(token1);}
();
String[] tempsArray = Array(new String[0]);
for (String s: tempsArray) {
(s);}}}Here, we defined a simple ReadFile class. In the main function, we started by declaring a string named token to store the lines in the file while reading through it. Next, we use the in-built Scanner class and send the file name in a File instance with a delimiter. Next, we declare an array to store the lines, followed by for loop, iterating through the files and appending all the array lines. Lastly, we print the is a simple example of working on text data. When it comes to image data, Java does have tesseract support. However, the features are limited. Looking to parse and extract information from files? Head over to Nanonets to automate the process of parsing, extracting, exporting and organizing information from your files! Automating the Process of Parsing Info or Data from FilesIt’s all about reducing human effort, time and expenses for any business. For this, automation is the only solution. Now, let’s discuss some ways we can automate parsing files with modern assic Software: Building software to automate stuff is a simple solution. This software has all the basic operations and instructions to get the job done. But, these are not scalable and extensible. The features will be limited and will be using local storage to save all the data. Therefore, a parser-based out of simple software can be used for small files that need to be regularly cleaned up. We can write conditions through which the files pass into, say, converting a simple PDF into JSON. We could use languages like Python or Java to read this file and perform specific operations and iterate it onto multiple ever, we can’t perform operations like parsing through tables or reading through images as they require more powerful libraries to be integrated, which might consume more computation power and data. Therefore, users prefer the cloud when it comes to parsing data from Applications: Web applications are utilised for UI to automate the file parsing process. These choose specific backend languages like PHP, Python, Java to operate on certain types of files. All the communication between the UI, backend, and database happens mainly through the databases. If the website is served on a powerful cloud solution, OCRs can also be integrated to perform all kinds of data parsing operations. However, this solution might be time-consuming, as it involves many steps and requests to consume all across the web. Sometimes, due to confidentiality requirements, businesses opt for on-prem solutions in which the software will be a third party application, but the database can be hosted and RPA: Robotic Process Automation (RPA) is one of the latest advances in automation. In RPA, robots take care of automating all the manual tasks instead of humans doing manual stuff. They are also embedded with intelligent algorithms, through which they’ll learn and minimise the error rates for every iteration. These robots can be connected with different data sources, APIs, and third-party integrations; this gives us an advantage in collecting and processing data for parsing Automation (Source)Looking to parse and extract information from files? Head over to Nanonets to automate the process of parsing, extracting, exporting and organizing information from your files! Example Use Cases of Parsing Data from FilesIn this section, we’ll look at a couple of use-cases on how file data parsers can help automate manual entry for your business. We’ll also be studying a rough outline of all the techniques involved for these specific llecting data from invoices and receipts: Invoicing is everywhere, from small businesses, startups to giant industries and corporations. Most of these organisations use excel sheets, save these in cloud drives and manually enter them into different data formats. If we have to search through any data, it’s highly impossible as most of these are saved in PDFs, PNGs or Document format. Also, many invoices contain tabular data in which details of products or services are in line items. Therefore, copy-pasting these would mess up the data ultimate solution to organise these invoice documents is to use a generic data parser to extract all the necessary information into a more readable format such as JSON or Excel/spreadsheet. But building a generic parser is challenging. Below are the essential aspects that we’ll need to take care of:Process different types of invoices into a readable raw there are any images or tables inside these invoices, those need to be saved separately as we’ll need to run an OCR to parse through ‘ll need to annotate all the important invoices, such as Invoice_Id, Invoice_Type, Billed_to, Billing_Adress, Total, Taxes an ideal parser with an OCR that can extract all the fields from the invoiceExport the extracted data into either a JSON format or Excel/spreadsheet. Digitising your invoices saves a lot of time and consumes less human work, eventually reducing the expenses spent maintaining the invoices. With expense minimisation and expedited payments, you’ll have more resources to invest in innovation, hire and improve your offerings, and conduct core business tomating KYC for Financials: A lot of KYC processes slow down onboarding customers in businesses. This drives up the overhead and lacks quality control. In such cases, introducing automation can streamline, improve, and drive llection Data form KYC Documents (Source)Fortunately, with a file data parser, we can make automating KYC documents much more effortless. The job of the file data parser is to parse through all the customer’s documents such as government-provided IDs, Professional IDs, Financial documents, etc. and store them in a much more reliable and unambiguous way. This helps the business to quickly review the customer’s documents and proceed with further are the steps for KYC process automation with data parsers:Gather required documents that need to process for KYCUse powerful OCRs to extract all the necessary text from these documents to create a datasetTrain deep learning-based models that can extract only necessary fields with NLPUse data parsers to convert the extracted text and save them in a more reliable format, for example, JSON or SpreadsheetsBuild software or export APIs that can automate this entire processLooking to parse and extract information from files? Head over to Nanonets to automate the process of parsing, extracting, exporting and organizing information from your files! Workflows and Integrations with File ParsingBuilding workflows allows us to connect with different solutions and automate processes. For example, consider the following scenario:We receive 10-15 emails every day that consists of invoices related to our store all these invoices into cloud storage in a particular folder named as the date invoices are, we rename each of these invoices with the invoice-id and the business, set notifications to remind about invoice due ually, doing this with a data parser takes a lot of time, and tasks like downloading invoices, uploading them to cloud storage, and renaming them with parsers can be annoying. Therefore, to automate the boring stuff, we could build workflows using different workflows are mostly built on the cloud, which can talk to different services using APIs and Webhooks. If you’re not a developer, there are products like Zapier that can connect with your data parser and perform particular tasks. Now let’s see how these webhooks and APIs flows with APIs: Currently, almost everything that’s on the web is communicated through APIs. Therefore, we can leverage these APIs to build powerful workflows and build data parsing solutions. Let’s discuss a few fundamentals. First, we build data parsers for working with massive data. Therefore, the starting point is to use cloud storage for all of your data, and for this, we need not build a data centre. Instead, we can subscribe to an online cloud service and leverage APIs for your data. We can’t download every file from the cloud and run OCRs to extract data, but building and maintaining one online is a complicated process. For these, we can directly connect the APIs from cloud storage to the OCR solution such as Nanonets, AWS, GCP. Additionally, you need not choose a different tech stack for these; several SDKs provide a wide range of support. Lastly, to save the output from the data parsers into the desired format, say excel spreadsheets or software, we can use the response from these APIs and transform them based on our ‘s a how the Nanonets API looks like:Python Code: This small snippet will be taking the invoice as input and then return the crucial fields from it. We can also test this on our data; for this, we’ll need to create a model on Nanonets and export it as an API using any desired requests
url = ” + ‘REPLACE_MODEL_ID’ + ‘/ImageLevelInferences? start_day_interval={start_day}¤t_batch_day={end_day}’
response = quest(‘GET’, url, (‘Auzt3_feYVGJhuYiBUnbun5c8qQXS_rt’, ”))
print()Ouput:{
“moderated_images”: [
{
“day_since_epoch”: 18564,
“hour_of_day”: 15,
“id”: “00000000-0000-0000-0000-000000000000”,
“is_moderated”: true,
“model_id”: “category1”,
“predicted_boxes”: [
“label”: “invoice_id”,
“ocr_text”: “877541”,
“xmax”: 984,
“xmin”: 616,
“ymax”: 357,
“ymin”: 321}],
“url”: “uploadedfiles/00000000-0000-0000-0000-000000000000/PredictionImages/”}],
“moderated_images_count”: 55,
“unmoderated_images”: [
“day_since_epoch”: 18565,
“hour_of_day”: 23,
“is_moderated”: false,
“model_id”: “00000000-0000-0000-0000-000000000000”,
“label”: “seller_name”,
“ocr_text”: “Apple”,
“unmoderated_images_count”: 156}Looking to parse and extract information from files? Head over to Nanonets to automate the process of parsing, extracting, exporting and organizing information from your files! Nanonets Advantage in Parsing Info from FilesNanonets is an AI-based OCR software that automates cognitive capture for intelligent document processing of invoices, receipts, ID cards and more. Nanonets uses advanced OCR, machine learning and deep learning techniques to extract relevant information from unstructured data. It is fast, accurate, easy to use, allows users to build custom OCR models from scratch and has some neat Zapier integrations. Digitise documents, extract data fields, and integrate with your everyday apps via APIs in a simple, intuitive ’s why you should use Nanonets as data parsers:Pre-processing: If your documents are poorly scanned or in various formats, you need not worry about preprocessing them. Nanonets automatically processes documents based on alignments, fonts, and image Processing: You can always perform post-processing on outputs and export the data into desired formats such as CSV, Excel Sheets, Google tomation: If you are working with loads of data, nanonets has some pre-installed installations using Zapier, UiPath etc.
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
Frequently Asked Questions about what does it mean to parse a file
Why do we parse file?
A data parser helps us turn unstructured data/un-readable data into structured data. Data Parsers are also used to convert data from one type to another. For example, let’s assume we have an HTML file, and we would need to convert it into a PDF file.Aug 10, 2021
What is parse example?
Parse is defined as to break something down into its parts, particularly for study of the individual parts. An example of to parse is to break down a sentence to explain each element to someone. … Parsing breaks down words into functional units that can be converted into machine language.
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 is also used in psycholinguistics when describing language comprehension.
