Parsing or CV analysis: definition and functioning

parsing ou analyse cv


Well known to recruiters, CV parsing is a method of automatically reading CVs. It is used to pre-qualify candidates and find the best talents in a minimum of time. Let’s take a look at this essential digital recruitment tool!

Resume parsing, definition

Also called “résumé parsing”, parsing is a technique for automatically analyzing résumés. In English, “to parse” literally means “to analyze”.

In concrete terms, parsing consists of extracting in a few seconds the key data contained in a document – in a CV, this would be the name, the job or the skills of a candidate. The goal: to analyze and sort the CVs in a minimum of time in order to identify high-potential profiles that correspond to a specific job offer. 

How does CV parsing work? 

This analysis method is based on artificial intelligence – in particular Deep Learning.

💡 As a reminder, Deep Learning refers to a set of automatic learning methods that mimic the human brain. For example, Deep Learning can recognize letters in words or shapes present in images.  

CV parsing allows to extract all key information about a candidate. Then, this information is collected, sorted and classified in a table or recruitment software in a few seconds.  Candidate files are then created and filled in thanks to the automatic filling in of the information by the parsing. 

Important information that can be extracted by a parsing tool is : 

  • the technical skills of a candidate
  • his or her professional background
  • level of experience
  • his contact information
  • academic training
  • his or her certifications, if any

This list is of course not exhaustive! 

What does a recruitment process with parsing look like? 

Once the needs of the teams have been collected, the recruiter generally posts its job offers online. Interested candidates then apply by submitting their CVs. This is where parsing comes in. Once the recruitment software with the parsing function receives the CV, it analyzes it in the light of precise keywords. Then, the tool synthesizes the extracted data into a candidate file.

It’s easy to see why: CV parsing saves recruiters from having to manually fill in their tracking table with key information about candidates (name, specialization, academic background, years of experience, etc.).  

💡 Note that parsing should allow for a pre-selection of resumes, which can be refined later by the recruiting professional. 

CV analysis: advantages… and disadvantages! 

As it eliminates time-consuming recruitment tasks such as the analysis and sorting of hundreds of resumes, parsing greatly facilitates the management of applications. It also saves a significant amount of time, allowing recruitment professionals to dedicate more time to higher value-added tasks, such as conducting job interviews. 

💡 As we’ve seen above, resume parsing allows for faster candidate pre-screening. 

But beware: automatic CV processing also has its drawbacks. For example, some resume formats are harder to analyze than others. Thus, a recruiter may miss some candidates with less conventional resumes, because the analysis tool will have more difficulty extracting the data… 

Let’s also remember that automatic data analysis is not an exact science. Therefore, error is not impossible.  

Finally, we must not forget that parsing offers an incomplete vision that does not necessarily take into account the soft skills of a candidate, his or her possible atypical background… This method of analysis leads to the selection of “typical backgrounds” at the expense of personalities.

💡 Of course, resume analysis remains an aid for the recruiter, but this tool can in no way replace the analysis of a professional. 

Benefit from CV analysis with an ATS

By using an ATS (Applicant Tracking System) like JobAffinity and partnership with Textkernel extract, you automatically get a parsing option! Indeed, most recruitment software allows recruiters to automate their application management, with automated functions for filtering and pre-selecting resumes

Do not hesitate to contact our teams to learn more!

See articles in the same category