Resume Screening using Machine Learning
DOI:
https://doi.org/10.32628/CSEIT2410275Keywords:
K-Nearest Neighbors, KNN, Machine Learning, NLP, NLTK, One v/s Rest, Support Vector MachinesAbstract
This study explores the utilization of Machine Learning (ML) and Natural Language Processing (NLP) in automating the resume screening process. Traditional methods, often manual and subjective, fail to efficiently manage the volume and variety of resumes. By employing NLP techniques like named entity recognition and part-of-speech tagging, coupled with ML classifiers such as K-Nearest Neighbors and Support Vector Machines, we propose a system that enhances the precision of candidate selection while significantly reducing time and effort.
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B. Kinge, S. Mandhare, P. Chavan, and S. M. Chaware, "Resume Screening Using Machine Learning and NLP: A Proposed System," in International Journal of Scientific Research in Computer Science, Engineering and Information Technology, vol. 253, doi: 10.32628/CSEIT228240 DOI: https://doi.org/10.32628/CSEIT228240
A. K. Sinha, M. A. K. Akhtar, M. Kumar, and S. Upadhyay, "Resume Screening Classification using Artificial Intelligence and Natural Language Processing," Name of Journal, doi: 10.48047/ecb/2023.12.si4.130
P. K. Roy, S. S. Chowdhary, and R. Bhatia, "A Machine Learning approach for automation of Resume Recommendation system," in Procedia Computer Science, vol. 167, pp. 2318-2327, Jan. 2020, doi: 10.1016/j.procs.2020.03.284 DOI: https://doi.org/10.1016/j.procs.2020.03.284
D. L. Padmaja, Ch. Vishnuvardhan, G. Rajeev, and K. N. S. Kumar, "Automated Resume Screening Using Natural Language Processing," in Journal of Emerging Technologies and Innovative Research (JETIR), vol. 10, no. 3, Mar. 2023
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