Automated Resume Parsing and Job Domain Prediction using Machine Learning

Md. Amir Khusru Akhtar · Mohit Kumar

14 citations0/0 files storedIndian Journal of Science and Technology

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AI and HR Technologies Topic Modeling Advanced Text Analysis Techniques

Abstract

Objectives: This study aims to develop an efficient approach for parsing resumes and predicting job domains using natural language processing (NLP) techniques and named entity recognition to enhance the resume screening process for recruiters. Methods: The proposed approach involves preprocessing steps, such as cleaning, tokenization, stop-word removal, stemming, and lemmatization, implemented with the PyMuPDF and doc2text Python modules. Regular expressions and the spaCy library are utilized for entity recognition and name extraction. The model achieved a prediction accuracy of 92.08% and an F1-score of 0.92 on a dataset of 1000 resumes. An ablation experiment assessed the contributions of different factors. Findings: The approach demonstrated a high prediction accuracy of 92.08% and F1-score of 0.92 for job domain prediction, effectively identifying relevant job domains from resumes. Evaluations on individual job domains showed excellent precision and recall scores, validating its applicability. Preprocessing techniques significantly improved accuracy, while the integration of regular expressions and spaCy enhanced the model’s performance. This approach automates resume screening, reducing recruiters’ workload, saving time and effort, and improving candidate selection and the hiring process. Novelty: This study introduces a novel approach combining NLP techniques, regular expressions, and entity recognition for resume parsing and job domain prediction. This integration enhances accuracy and efficiency, offering a unique solution for resume screening. Keywords: Resume parsing; Job domain prediction; Entity recognition; Machine learning; Natural Language Processing

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2020A Machine Learning approach for automation of Resume Recommendation system 2018Text Preprocessing 2021The procedural and interpersonal justice of automated application and resume screening 2022Resume Screening using Machine Learning and NLP: A proposed system 2020Methodology for resume parsing and job domain prediction 2023A domain adaptation approach for resume classification using graph attention networks and natural language processing 2022A Bibliometric Perspective on AI Research for Job-Résumé Matching 2021Resume Parsing based on Multi-label Classification using Neural Network models

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