How to Choose the Best Parser: Features to Look For
With zettabytes of data stored in public and private repositories worldwide, it’s no surprise that finding information relevant to your business can become an issue. But that’s where data parsing comes in, helping you extract information from unstructured data. However, knowing how to parse data isn’t enough.
Table of Contents
You should also ensure that you’re using the best parser for the job. Modern parsers have a wide range of features, such as high accuracy, support for multiple languages, fast speed, and easy integration.
But are these features enough? Do you need specific parsers for certain jobs? Let’s discuss this in detail below.
How to Parse Data For Your Business?
First, you must understand what parsing is.
Parsing means converting formatted or unstructured text into structured data. A parser is a tool that breaks down data into its components, narrowing it down to the information most relevant to you.
In parsing, a data structure may be any suitable representation of the source text information. Here are three main data structures:
Common in XML, JSON, and HTML parsing, the tree type is a data structure that can represent hierarchical relationships between objects.
Suppose you want to parse an HTML document. Tree-type parsing will allow you to analyze different elements of the page. These could include the footer, body, sidelines, and header.
The output of a tree-type data structure is called an abstract syntax tree or a parse tree. In HTML parsing, it’s called DOM (Document Object Model).
CSV file parsing is the best way to parse PDF files since it results in a list of record objects or values. For instance, if you want to parse a PDF file containing the addresses of all your customers for segmentation, you can assign objects to every value in the file.
Let’s say the data has four columns. You can assign an object to each of them, such as state, city, address, and name.
Graph type parsing allows you to analyze structured information from a graph. In this case, the graph is the collection of vertices or nodes connected to each other by edges.
The nodes represent entities in the data, whereas the edges showcase their relationship.
Suppose you have a graph of social networks between your target customers. Every customer is a node on the graph. The edges may represent different connections between these customers, such as the same demographic or similar interests.
Marketers and advertisers can use this graph to uncover relationships between their target audiences. For instance, if there’s a strong connection between two nodes (customers) in the graph, they likely influence each other’s purchase decisions.
How to Choose the Best Parser?
If you know how to parse data, your next step should be to choose the best parser generator. The right parser for your needs will depend on your requirements. But here are some standard features of the best parsing software.
Support for various formats
Before you go into the intricate details of a parser, check if it supports multiple formats. By formats, we mean HTML, DOC, PDF, etc.
Depending on your use case, you might have to feed different document formats to a parser. For example, if you need a parser for resume checking, you might need a parser that recognizes DOC and PDF files.
Meanwhile, a parser for web scraping must have HTML support. In web scraping, you extract data from the web by retrieving HTML content. After that, you have to parse the desired information from this data.
Besides using a parser, it also helps to invest in a scraping API (Application Programming Interface) for web scraping. A scraping API, such as Scraping Robot, offers a programmatic and structured way to retrieve data from the web.
So, you won’t have to write complex scripts for web scraping. All you need to do is choose the websites you want to scrape and select the output format, such as CSV or JSON.
Recognition of human context
Another feature of the best parsing software is that it should recognize human context. But what does that mean?
The parser must pick up common words or phrases humans use. For example, if you’re using a resume parser, it should be able to recognize phrases like “Master’s degree in Pathology.”
Here are two reasons why recognizing human context is imperative for a parser.
Human language can be ambiguous. Often, the same words have different meanings. For example, “bark” can be a dog’s sound or the outer layer of a tree.
It’s likely that the data you parse may contain homonyms. The parser should be able to differentiate between different meanings and show results based on the context in which the word is used.
Suppose you say this: ”Sarah went to a concert last night. She is quite tired today and will take a day off.”
In this text, “she” is an example of an anaphoric reference since it refers to a previously mentioned entity – Sarah. If the parser doesn’t understand human context, it will struggle to recognize who “she” is.
But a parser with human context recognition would know it’s Sarah.
The taxonomy library of a parser represents the terms a parser can identify in formatted data. Ideally, you should choose a parser with a large taxonomy library.
Let’s take the same example of a resume parser. As an HR manager, you may have to recruit people for different posts in your organization.
You cannot rely on a parser with only a tech or IT taxonomy library. Rather, the parser should have a diverse taxonomy library that can parse for keywords related to all departments in your company.
Once you learn how to parse data, you can integrate your parser with the existing systems in your organization. For example, an HR manager can integrate the parser with the company’s Human Resource Management System.
Meanwhile, marketers may integrate it with Customer Relationship Management systems, while the IT departments may integrate it with their in-house databases.
Alternatively, you can use a web scraping API like Scraping Robot with a built-in metadata parser. It eliminates the need for you to build a separate parser for retrieving desired metadata information.
Let’s say you’re looking for the best email parser to go through the thousands of emails in your inbox. You can’t rely on software that takes hours to find the necessary information.
Instead, look for a parser that processes data quickly, saving time and effort. Modern AI-based parsers take as little as a few seconds to parse a document.
If you have the following or similar use cases, you’ll need a multilingual parser:
- Machine Translation: A multilingual parser helps in semantic and syntactic analysis of the source text. In this way, it allows higher-quality translations, letting you preserve context and sentence structure across languages.
- Sentiment Analysis: For a global consumer sentiment analysis, you need a multilingual parser to process natural language in any language.
- Cross-Language Search: With a multilingual parser, you can search for documents and data across languages. That eliminates the need for translation before searching for information.
If you know how to parse data across languages, you can also use it for text classification and grammar checking.
Support for different structures
Regardless of the document format, it’s likely that the information inside will be in different structures and layouts. Suppose your company uses log files to record suggestions in performance analysis.
The best log parser should be able to read and understand the information in different structures. For example, if the log levels, language, timestamp placement, or data format is different across logs, the log parser should still be able to extract your desired information.
The key here is to not fall for the monetary perk of a free parser. Even the best free log parser won’t have the features you need. Invest in a paid log parser for the best results.
Conversely, you can write your script using the best parsing language for use cases — Python in most instances.
How to Parse Data Using the Right Parser?
So, you’ve selected the right parser. Now, you want to know how to parse data using it. Here are some quick steps.
Install the parser library
The library you install will depend on the parser you’ve selected. You can install a library using a package manager like Gem for Ruby and Pip for Python.
Import the library
Import the necessary classes or modules from the library to your code.
Get the data
Choose the data you want to parse. It may be a web page, a PDF file, or any other semi-structured data source.
Create a parser object
Use the parser library to create a parser object. The object is usually an instance of a class in the library.
Parse the data
Determine the method your parser uses to load data. Use it to load the data and extract the information you need. You may need to refer to the documentation of the library you selected for more information.
Now that you know how to parse data, you can save the retrieved information into a database. Further, you can manipulate it for insights and informed decision-making.
As long as you know how to parse data, you’re all set to get the information you need from any data structure. A parser with features like multilingualism, fast speed, and customizable syntax will streamline data parsing.
After selecting the parser for your use case, you should practice with it to learn how to parse data. For example, you can feed it different data sources, search across languages, check its processing speeds, and ensure it’s compatible with your software platform.
Register today to get 5k free credits.
The information contained within this article, including information posted by official staff, guest-submitted material, message board postings, or other third-party material is presented solely for the purposes of education and furtherance of the knowledge of the reader. All trademarks used in this publication are hereby acknowledged as the property of their respective owners.