Applying BERT-Based NLP for Automated Resume Screening and Candidate Ranking

Abstract

In this research, we introduce an innovative automated resume screening approach that leverages advanced Natural Language Processing (NLP) technology, specifically the Bidirectional Encoder Representations from Transformers (BERT) language model by Google. Our methodology involved collecting 200 resumes from participants with their consent and obtaining ten job descriptions from glassdoor.com for testing. We extracted keywords from the resumes, identified skill sets, and ranked them to focus on crucial attributes. After removing stop words and punctuation, we selected top keywords for analysis. To ensure data precision, we employed stemming and lemmatization to correct tense and meaning. Using the preinstalled BERT model and tokenizer, we generated feature vectors for job descriptions and resume keywords. Our key findings include the calculation of the highest similarity index for each resume, which enabled us to shortlist the most relevant candidates. Notably, the similarity index could reach up to 0.3, and the resume screening speed could reach 1 resume per second. The application of BERT-based NLP techniques significantly improved screening efficiency and accuracy, streamlining talent acquisition and providing valuable insights to HR personnel for informed decision-making. This study underscores the transformative potential of BERT in revolutionizing recruitment through scalable and powerful automated resume screening, demonstrating its efficacy in enhancing the precision and speed of candidate selection.