Resume parsers or CV parsers are a brilliant tool for companies actively hiring new employees and who want to streamline the candidate screening process. According to Glassdoor, most talent acquisition leaders consider selecting the right candidates from a large applicant pool as the hardest part of their job. Parsing technology allows recruiters to gather, store, and organize large resume databases. Resume data can be easily analyzed and searched.
How to parse resume
So, what is resume parsing? Resume parsing is the conversion of a resume in a Microsoft Word or PDF format into a data structure suitable for storage, reporting, and manipulation by software.
Resume parsing begins by uploading applications into the parsing software. CVs are usually uploaded automatically, but you can do it manually as well. Once the resumes are uploaded, CV parsing tools scan each document and extract keywords from job description. Keywords include all information about education, job skills, specific work experience, professional certifications and contact information relevant to a recruiter’s needs.
By screening and ranking the resumes with the relevant information, and eliminating resumes without it, parsing software can save recruiters the thousands of hours otherwise required to manually check each individual application. For instance, Employa can process an unlimited number of CVs simultaneously, reducing the parsing and screening phases to a few seconds.
The main challenge of parsing is that interpreting language and information is extremely difficult, making it hard for Artificial Intelligence algorithms to sort through a large dataset. Language is both highly diverse and uncertain. For example, the ways to write down a date differ between countries, and the same word means different things depending on the context.
Resume parsing software: an easy way to reduce the time and effort needed for CV screening
Resume parsing tools are often a part of Applicant Tracking Software (ATS) platforms and extensions. The best resume parsing tools work much quicker than even the most efficient professional recruiters, cutting the CV screening process from days into seconds while replicating human accuracy at a rate of 95%.
There are several stand-alone parsing tools like Sovren CV Parser or DaXtra Parser, and other parsers included as a feature in huge ATS systems like ZohoRecruit or Hiretual. So, how to choose the parser that suits you best? First of all, it depends on your company’s size.
If you are a startup or small business, you probably don’t need an expensive ATS stuffed with features. Instead, your best bet is to choose a stand-alone tool, that covers all your hiring needs. Here are a few of the most popular tools for small and medium businesses. Check them out and see if they work for you.
If your company is a big enterprise player, you have two options: If you already have an ATS system you are satisfied with, it doesn’t make sense to switch to another one. Check a trustworthy review website like G2Crowd and find a stand-alone parsing tool that fits your needs. Alternately, you can choose resume management software with all the add-ons needed for your hiring process.
According to the latest data from Statista, there are about 6.44 million job openings in the United States. And that’s during the pandemic, not even considering post-pandemic demand. It’s hard to imagine processing even 10% of these resumes manually! John Sumser, a principal analyst at HRExaminer, an online newsletter that focuses on HR technology, estimates that on average, companies get five to seven a day from vendors using data science to address HR and hiring issues. To keep above the surface of this informational waterfall, companies need to think carefully about how to select text parser software and start streamlining the hiring process.
We highlighted a few tips on how to choose the best CV parser. The best CV parsers:
- Work with all CV formats
- Have an accuracy level above 90%
- Integrate easily with your existing recruiting software or Applicant Tracking System
- Correctly detect regions and languages
- Identify candidate skills and experience accurately
- Extract the required information
A significant benefit of resume parsing tools is preventing bias and discrimination. «Establishing neutral practices that result in good hires is not only basic to good management but the only real defense against claims of adverse impact and discrimination. Other than white males under age 40 with no disabilities or work-related health problems, workers have special protections under federal and state law that prohibit hiring practices that may have an adverse impact on them. As a practical matter, that means if members of a particular group are less likely to be recruited or hired, the employer must show that the hiring process is not discriminatory.
«The only defense against evidence of adverse impact is for the employer to show that its hiring practices are valid—that is, they predict who will be a good employee in meaningful and statistically significant ways—and that no alternative would predict as well with less adverse impact. That analysis must be conducted with data on the employer’s own applicants and hires.»Peter Cappelli, George W. Taylor Professor of Management at the Wharton School and a director of its Center for Human Resources.
The technical side of resume keyword scanner
Applicant tracking systems and stand-alone parsing tools often include a resume keyword scanner. As the name suggests, keyword scanners are used to search resumes for specific keywords set by recruiters and businesses and sort candidates into groups based on the results. Keyword scanners are widely used by job sites. A CV parsing tool or resume keyword scanner can be created using Python parsing: popular libraries are available on an open-source basis and tons of manuals are written on this subject.
Resumes are a great example of unstructured data; each CV has unique data, formatting, and data blocks. To understand how to parse data in Python, check this simplified flow:
1. Reading the Resume
- Installing pdfminer
- Installing doc2text
- Extracting text from PDF
- Extracting text from doc and docx
2. Extracting Names
- Installing spaCy
- Rule Based Matching
3. Extracting Phone Numbers
4. Extracting Emails
5. Extracting Skills
- Installing pandas
- Word Tokenization and Extraction
6. Extracting Education
The main issue with parsing text in Python is that normally recruiters get CVs in pdf, doc, or docx, which require conversion to plain text.
CV parsing is also available for companies without a developed talent pipeline who don’t get a bunch of CVs every day. There are many job board websites and social media resources focused on hiring, inviting companies of any size to use their data to find the perfect candidate. Specific parsing tools called web scrapers are ready to help you with this challenge. Here are a few of the most popular tools to extract specific data from websites:
Python Scrapy — is a free and open-source web-crawling framework written in Python. Originally designed for web scraping, Python can also be used to extract data using APIs or as a general-purpose web crawler. Scrapy architecture is built around «spiders», which are self-contained webpage crawlers that are given a set of instructions. Following the spirit of other «don’t repeat yourself» frameworks, it makes it easier to build and scale large crawling projects by allowing developers to reuse their code.
Beautifulsoup — a Python library for getting data out of HTML, XML, and other markup languages. Say you’ve found some web pages that display data relevant to your research, such as date or address information, but that don’t provide any way of downloading the data directly. Beautiful Soup helps you pull particular content from a webpage, remove the HTML markup, and save the information. It is a tool for web scraping that helps you clean up and parse documents you have pulled down from the web.
Using Artificial Intelligence tools in recruiting is a given. But we need to consider that automating the hiring process should bring us efficiency and effectiveness rather than merely adding new software that consumes additional time and money. In this huge pool of smart recruiting tools, each company deserves to find one that suits it best.