NLP-Based AI-Driven Resume Screening Solution for Efficient Candidate Selection

Finding the appropriate candidate for a position can be a difficult and time-consuming effort. The sheer volume of resumes and an expanding applicant pool makes it difficult for hiring managers to find the best-suited applicants quickly and accurately. Effective candidate selection is critical in today's highly competitive employment market. Traditional resume screening processes are time-consuming and prone to bias, resulting in poor recruiting judgments. This study presents an AI-driven resume screening system based on Natural Language Processing (NLP) to address these difficulties. A proprietary spaCy Named Entity Recognition (NER) library is used to extract key data from resumes, including names, organizations, job titles, skills, experiences, and education. The system employs data preprocessing techniques such as data cleansing, structuring, augmentation, text normalization, tokenization, part-of-speech tagging, stop word removal, and lemmatization/stemming to assure correctness and reliability. Following that, the proprietary NER model computes scores for each application based on the retrieved data, resulting in a ranked list of the best applicants. A comparison study was done to compare the suggested approach to traditional resume screening methods. In terms of accuracy, efficiency, and fairness, the results showed that the NLP-driven resume screening solution surpassed traditional approaches. Furthermore, it lowered the time and effort required for resume screening, allowing recruiters to focus on more important activities like candidate assessments and interviews.