Demystifying Resume Parsers: How AI Reads Your CV
Understand the underlying parsing algorithms (NLP, tokenization, heuristics) used by Greenhouse and Workday.
Ever wondered what happens when you drag and drop your PDF CV into a job portal? Behind the screen lies a complex parsing engine that uses Natural Language Processing (NLP) and heuristics to disassemble your professional history.
1. File Extraction and OCR
First, the parser reads the file. If it is a clean text-based PDF or Word document, it extracts the characters directly. If it is an image, it uses Optical Recognition (OCR) to guess the text. OCR is highly error-prone, which is why you should never upload a rasterized image of your resume.
2. Heuristics and Segmentation
The parser looks for section dividers like "Experience" or "Skills" to divide your resume. If you use non-standard headers like "My Journey" or "Where I Have Been," the parser may fail to segment your document, causing your work history to be cataloged incorrectly.
3. Entity Extraction and NLP matching
Once segmented, the NLP model scans for specific entities: job titles, dates, companies, and skills. It parses dates to calculate your years of experience in specific roles, matching them against minimum requirement filters set by the recruiter.
Key Takeaway
Knowing that parsers are text-based pattern matchers, keep your layout standard, your headings conventional, and your files text-searchable.