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What does parsing words mean?

Parsing is the process of analyzing a string of symbols, either in natural language, computer languages, or data structures, conforming to formal grammar rules. It involves dividing a sentence into grammatical parts and identifying the parts and their relations to each other. It can refer to the act of describing a word grammatically by stating the part of speech and explaining the inflection.

Source: Wikipedia

What is parsing of data?

Data parsing is a method where one string of data gets converted into a different type of data. So let’s say you receive your data in raw HTML. A parser will take the said HTML and transform it into a more readable data format easily read and understood such as plain text. However, not all the information gets converted during the parsing process and programs tend to have their own sets of rules for parsing information.

Data parsing is used to crawl information from large datasets and structuring it in a way humans can understand. Traditional data parsing is carried out on HTML files where the parser converts HTML text into readable data. But not all parsers work the same and there are some rather significant differences in parsing technologies. Data parsing brings several benefits for businesses from automated data extraction, improved visibility, cutting costs, and boosting employee productivity.

In computer programming, parsing involves analyzing a string of symbols, special characters, and data structures through the use of Natural Language Processing (NLP). When you talk about extracting in parsing, you are talking about structuring information from data sets and giving it meaning by means of organizing it on the basis of user-defined rules.

What are the 3 types of parsing?

Tree type is a common and standard choice for XML parsing, HTML parsing, JSON parsing, and any programming language parsing. The output tree is called Parse Tree or Abstract Syntax Tree. In HTML context, it is called Document Object Model (DOM).

A CSV file parsing can result in a List of values or a List of Record objects.

Graph Type is a choice for natural language parsing.

A piece of program that does parsing is called Parser.

There are two approaches to data parsing

Grammar driven data parsing

In ​​Grammar driven data parsing, the parser makes use of a set of formal grammar rules for the parsing process. Sentences from unstructured data get fragmented and are transformed into a structured format. The issue with grammar-driven data parsing is that the models are not robust enough. You can overcome this 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. It assigns a number of analyses to a particular string and even resolves disambiguation problems that traditional methods of parsing face.

Data-driven data parsing

In data-driven data parsing, you make use of a probabilistic model and bypass deductive approaches of text analysis that are generally employed by grammar-driven models. The program applies rule-based techniques, semantic equations, and Natural Language Processing (NLP) for the purpose of sentence structuring and analysis. Data-driven data parsing makes use of statistical parsers and modern treebanks to obtain broad coverage from languages. Parsing conversational languages and sentences that require precision with domain-specific unlabelled data come under the scope of data-driven data parsing.

What do parsers do?  

A well-made parser will distinguish which information of the string is needed, and in accordance to the parsers, pre-written code, and rules, it will pick out the necessary information and convert it into JSON, CSV, or a table, for example.

It’s important to mention that a parser itself is not tied to a data format. It’s a tool that converts one data format into another, how it converts it and what depends on how the parser was built.

Why do we need parsing?

We need parsing because different entities need data to be available in various forms. Parsing allows transforming data in a way that can be understood by specific software. The obvious example is programs — humans write them, but computers must execute them. So, humans write them in a form that they can understand; then, software transforms them in a way that a computer can use.

Where is parsing used?

Parsers are used for many technologies, including:

  • Cognitive Search
  • Java and other programming languages
  • HTML and XML
  • Interactive data language and object definition language
  • SQL and other database languages
  • Modeling languages
  • Scripting languages
  • HTTP and other internet protocols

How does parsing work?

Parser analyses source text against the format prescribed. If source text does not match against format error is thrown or returned.

If source text does not match against format, an error is thrown or returned.

If matches, then “data structure” is returned.

How does parsing work in Engati?

Parsing plays a vital role in Engati, especially when it comes to Smart Responses. A Smart Response is a tool that streamlines the current chatbot setup experience to make it easier for you to set up a chatbot as quickly and effectively as possible. 

There are currently 4 ways to set up a smart response. The one we’ll be focusing on is DocuSense.  DocuSense aims to provide you the ability to upload documents used to answer chatbot users’ queries. It also minimizes the bot training effort and offers the option to combine answers to user queries from multiple FAQs and cognitive search sources. This widens the capability of the bot to provide more appropriate responses, reduces effort and time consumed, leading to a more intelligent bot with lesser efforts.

Customers ask complex questions, like finding a specific policy or questions about a particular guideline, which can’t be answered with simple FAQ matching. Adding FAQs for these particular questions may get cumbersome, so instead, we’ve given you the option to directly upload all of your policies to our NLP Engine. 

The more information you feed the engine, the better it becomes. As customers ask questions, the NLP Engine parses through your document and matches the string to provide an answer. 

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