What is resume parsing?
What is resume parsing?
A ‘parse resume’ definition we can use is ‘the process by which technology extracts data from resumes.’ This means that the job of the parser is to extract the key components of your CV, such as your name and email, the degrees you hold, the skills you have and your work experience. Which is pretty much what we are building with Workable. We’re very good at this but honest enough to admit that it’s hard. The facility for language of even a modern-day resume parser hasn’t yet reached human levels. In other words, you’re no longer penning a resume for someone who might prize quirkiness, Pharaonic paper or originality; you’re writing it for a parser, which wants you to follow standards.
Once the parsing tool is finished with a document, you’ll be able to easily search the resume data for specific keywords or phrases, thanks to advancements in machine learning. The program used can also search for these terms and works to bring relevant resumes and applicants front and center as you search for the right candidate.
It’s also possible for you to tailor the fields and forms to gather specific information that isn’t always included in traditional resumes or cover letters, like languages, community service, or references.
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 with our competitors 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. CV parser can reach up to 90% accuracy.
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.
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.
In terms of accuracy, these are better than a keyword parser but not as accurate as a grammar parser.
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