Automating Cyrillic Resume Parsing: Transforming HR Efficiency in the Pre-AI Expansion Era
Problem
In a time when NLP models were not as accessible as today, a leading Eastern European HR company faced a pressing challenge: they received a high volume of resumes in Cyrillic and struggled to extract essential applicant information accurately with their limited resources. Manual efforts were error-prone and inefficient, requiring an automated solution for the extraction process.
Solution
In collaboration with our client, Data Masters developed a groundbreaking solution. We created a sophisticated CV parser capable of extracting crucial information from each resume in the Cyrillic alphabet. Our solution covered key elements:
- Applicant Details: Extract the applicant’s first name, last name, date of birth, and address.
- Educational Background: Capturing educational history
- Work Experience: Extracting work experience details.
- Interests: Identifying applicant interests.
Key Components
- Rule-Based Approach: We designed precise rules to extract structured information, guaranteeing accuracy in capturing crucial details.
- Machine Learning Model: Using machine learning, we fine-tuned our model to handle unstructured data and adapt to unique resume formats.
Results
Our CV parser was thoroughly tested on 20,000 Cyrillic alphabet resumes, yielding remarkable outcomes:
- Accuracy: Achieving an average accuracy rate of 78% for extracting each type of information, significantly reducing errors.
- Resource Optimization: Our solution allowed the client to process resumes more efficiently, eliminating the need for extensive manual work.
- Error Reduction: The percentage of errors resulting from manual extraction was substantially reduced, improving data quality.
