The recruiting process always experiences a thrill of complexity, requiring countless steps, ranging from posting jobs to having new hires onboard. It’s estimated that companies in the US take 23 days on average to successfully hire a new employee. Surprisingly, resume screening turns out to be the most time-consuming part.
The challenge of HR, job sites, job boards, and headhunt agencies now lies in how to automate recruitment and boost productivity. There are many methods you can try out to simplify the procedure. One of the simplest ways is to make use of a resume/CV parser.
But what does a CV/resume parser really do?
Our today’s article will center on defining a CV/resume parser as well as its benefits. Then we’ll walk your way up to how a CV/resume parser works and the challenges behind parsing CVs.
A CV/resume parser refers to deep learning or AI technology that enables you to analyze data from a PDF/.DOC/.DOCX curriculum vitae or resume and extract this unstructured information into a structured format. This process allows you to store, report, and manipulate candidates’ data easily.
CV/resume parsers will create a candidate profile by extracting information into related fields, including personal details, working experience, education, skills, activities, and more.
You’re able to classify a large amount of data without human involvement. Some CV/resume parsers can even be integrated into software or a platform to efficiently organize and manage electronic resumes.
Basically, an AI resume parser automatically separates a candidate’s information on a CV or resume into numerous fields and attributes. The graph below explains how a resume parser works.
You need to upload all CVs/resumes of a certain position into the parsing platform. It depends on the software you use that you can input different file types like PDF, DOC, DOCX, etc.
After that, the tool will start scanning documents one by one and extract all information. The output will be Excel (.xls), JSON, or XML. JSON & XML files that consist of the application’s information well-organized in relevant fields.
In most cases, they include contact, educational qualification, work experience, achievements, and professional certifications.
Many reasons motivate you to use a resume parser to automate the CV screening process. Not only does it save you time but a CV parser also helps improve the hiring quality.
It’s meaningless to put a lot of your time and effort into scanning every resume. Imagine you receive 100 CVs for one job opening but 70-80% of them are unqualified. Then you waste weeks or months just manually going through all the resumes.
A CV/resume parser empowers you to quickly identify and determine who is the best fit for the position and your company based on specific factors. For example, if you want Senior JavaScript engineers only, after extracting information, the software will automatically remove all freshers and highlight your desired applications.
Performing repetitive tasks definitely exhausts HR staff. There isn’t a problem with screening a few resumes. What if you have hundreds or thousands to read through, it’d be a nightmare. This would be prone to human error without your notice.
Resume parsers give you a helping hand in automating the candidate profile completing and screening process. It frees you from manual work so you can save this precious time for more valuable activities like connecting with candidates and engaging employees.
Companies were urged to tackle sexually and racially discriminatory recruitment practices. And CV/Resume parsers are well thought out to help you achieve this goal. Since the CV/resume parser logically determines suitable applications, there are no chances for unconscious bias towards religion, race, color, age, gender, etc.
Many giant companies receive thousands to millions of resumes per year. It’s necessary for them to build a database to store this huge amount of data. CV/Resume parsing aims to structure candidate information so you can search for the right one with ease without wasting resources.
It seems simple for the human eye to screen documents and take out important information.
A computer system, on the other hand, needs to be trained continuously to get used to human languages. Each CV is written in a different language, layout, and composition. The only way for CV/resume parsers to extract information is to understand it properly, from the context to the relationship between words.
Consequently, we can’t proceed with rule-based parsers due to limitations in complex exceptions and ambiguity. We need to combine all CV parser methods, from keyword-based to grammar-based and statistical-based to deliver the most correct result.
There exists a fact that no rule-based approach is completely accurate. Take the Working Experience field as an example. We don’t have a specified format for writing dates which may cause the parser to read and process data in a different format. Plus, it depends on each organization or niche that the job titles can vary.
Plus, some candidates want to impress recruiters by designing eye-catching CVs/resumes with various graphic styles and elements. Frankly, this doesn’t work for a resume parser.
A resume parser is undoubtedly a necessary tool for recruiters and headhunters to sort resumes. It’s also an indispensable part of applicant tracking systems.
There are a plethora of CV parsers out there. However, picking the right one for your organization is not an easy task. Bear in mind to TEST the tool before making a decision. Don’t just choose one that you randomly find on Google. You should prepare a list of resumes to carefully check the parser based on numerous factors: speed, accuracy, and configurability.
Opportunity knocks! Don’t hesitate to automate the candidate profile completion and screening process with CV/Resume Parser now!
Data quality and quantity contribute significantly to the performance of AI and machine learning systems, as the algorithms rely heavily on large and accurate data to learn patterns and generate insightful predictions. Indeed, low-quality data generates inaccuracy in business decisions, leading to decreased business outcomes. A study by Vanson Bourne and Fivetran, an independent market […]
Read More >
