An Intelligent Applicant Tracking System Using NLP-Based Document Classification

The rapid growth of job applications in modern recruitment processes has made manual resume screening inefficient, time-consuming, and prone to bias. Organizations receive thousands of resumes for a single job opening, creating a critical need for automated and intelligent systems. This research proposes an advanced Applicant Tracking System (ATS) that utilizes Natural Language Processing (NLP) and machine learning techniques for automated resume classification, filtering, and ranking. The proposed system performs structured pre-processing, feature extraction using TF-IDF and semantic analysis, and classification using supervised machine learning algorithms. It also integrates a ranking mechanism that evaluates resume relevance based on job descriptions. The system not only reduces manual effort but also improves accuracy and consistency in candidate selection. Experimental evaluation demonstrates an accuracy of 87% in resume classification, indicating the effectiveness of NLP-based approaches in recruitment automation. This work contributes to the development of scalable, intelligent, and efficient hiring systems suitable for real-world applications.