Resume Parsing Approaches You Need to Know
Recruiters have been screening resumes manually for a long time now. They read through every candidate resume and evaluate them based on skills, knowledge, abilities and other desired factors. However, it would take a long time for the recruiter to go through each resume in detail. So, in practical world recruiters are forced into doing one of the two things They go through limited resumes, scan them thoroughly and take a pick out of them. They go through all (or most) of them, take a minimal amount of time to review them (some claim as low as 6 seconds), and pick whichever resumes can hook them. In both the cases, organizations lose out on quality candidates and recruiters waste their time and effort. So, How can one avoid it? This is where Resume parsing techniques or resume parser come into the picture.
What is Resume Parsing?
Resume parsing is a method of converting free-form resume documents in a more structured set of information suitable for mass storage, reporting, and editing. Above all, the main benefit of resume parsing for recruiters is to help them manage resume documents sent electronically.
Most commonly, recruiters will receive resumes in a PDF or word format, which is easy to read but not easy to manage when receiving hundreds of resumes a day. Resume parsing eliminates the need for manual processing. Resume parsers analyze a resume, extract the desired information, and insert the information into a database with a unique entry for each candidate. Once the resume has been analyzed, a recruiter can search the database for keywords and phrases and get a list of relevant candidates.
How Does Resume Parsing Work?
Resume parsing begins first by uploading all the applications for a job vacancy into the parsing software. This can be done manually, but many recruiters will set this to be done automatically.
The parsing software will then scan through each resume, extract the information relevant to the application and recruiter’s needs – this could include things like work experience, specific skills, educational background, qualifications/certifications, contact details, etc. Simultaneously, the resume parsing techniques or softwares can eliminate candidates without the relevant information – providing recruiters with a list of suitable candidates without needing to spend hours going through each resume manually.
Resume Parsing Techniques
When it comes to ML based resume parsing, there are three types of approaches that you can use, all with varying levels of accuracy and features.
A keyword-based resume parser will identify words, patterns, and phrases in the text of the resume or cover letter. It’ll use its own algorithm to find text around those words to read it correctly. This type is the simplest, but also the least accurate type of parser.
In terms of accuracy, it’s likely you won’t have higher than a 70% accuracy rate because it’s not able to extra information or data that isn’t surrounded by a specific keyword. If you’re dealing with an ambiguous keyword, like “writer,” they can guess incorrectly as it interprets it.
An example of how a keyword parser works would be scanning for something like a zip code and assuming the surrounding words are an address. Or it would scan for a date range and assume the text around it is an employment timeline.
When understanding the context of a resume or cover letter, a grammar-based parsing tool will use a large number of grammatical rules. They will combine specific words and phrases together to make complex structures as a way to capture the exact meaning of every sentence within a resume or cover letter.
With a grammar-based parsing tool, it’s possible to achieve up to 90% accuracy. However, they need a good amount of manual encoding by a skilled language engineer to get right. They’re generally more complicated than keyword parsers, while also being able to capture more detail. They also can easily distinguish between different meanings of words and phrases to better understand the context of the resume. To learn about candidate matching, visit here.
Statistical Resume Parsing Techniques
A statistical parser will apply numerical models of text to identify the structure of a resume or cover letter. Similar to a grammar-based parser, these work by distinguishing between contexts of the same word or phrase as a way to capture specific elements, like an address or a timeline.