AUTOMATED RESUME PARSING USING NAME ENTITY RECOGNITION
AUTHORS:
Mentor
Dr. S. A. Bhavsar
Affiliation
Department of Computer Engineering, Matoshri College of Engineering
DOI: 
SHARE THIS ARTICLE
CC BY 4.0 License:
This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
DOWNLOAD ARTICLE
REVIEW REPORT
CITE THIS ARTICLE
Abstract
The traditional hiring process often involves manually reviewing numerous resumes, making recruitment time-consuming and costly. To address this challenge, we propose an Automated Resume Parsing System using Named Entity Recognition (NER), an advanced Natural Language Processing (NLP) technique. Our system efficiently extracts key information, such as candidate names, skills, education, and work experience, from unstructured resume data, enabling structured representation and faster decision-making. By automating resume screening, our approach significantly reduces hiring costs and minimizes recruiter workload while improving accuracy in candidate selection. Furthermore, it enhances the efficiency of applicant shortlisting by filtering out irrelevant job applications. The system leverages machine learning models trained on diverse resume datasets to improve extraction accuracy and adaptability to various resume formats. Additionally, it integrates with applicant tracking systems (ATS) for seamless recruitment workflow automation. Experimental results demonstrate that our system achieves high precision in entity recognition, making it a valuable tool for modern recruitment platforms. The proposed solution not only optimizes the hiring process but also contributes to fair and unbiased candidate evaluation.
Keywords
Automated Resume Parsing Named Entity Recognition Natural Language Processing Recruitment Automation Applicant Tracking System Resume Screening Machine Learning
Article Information
PUBLICATION DETAILS
Review Type:
0
License:
CC BY-NC 4.0
Volume/Issue:
Volume Volume 10, Issue 01
Review Rounds:
2
Article Type:
Research Article
Publication Date:
Feb 20 2026
Article Metrics
Article Views
11
PDF Downloads
0
HOW TO CITE
Copy
Ghotekar, R. S. V. K. ,. (2026). Automated Resume Parsing using Name Entity Recognition. International Journal of Science, Strategic Management and Technology, Volume 10(01). https://doi.org/10.55041/ijsmt.v2i2.061
Ghotekar, Rajeshwari. "Automated Resume Parsing using Name Entity Recognition." International Journal of Science, Strategic Management and Technology, vol. Volume 10, no. 01, 2026, pp. . doi:https://doi.org/10.55041/ijsmt.v2i2.061.
Ghotekar, Rajeshwari. "Automated Resume Parsing using Name Entity Recognition." International Journal of Science, Strategic Management and Technology Volume 10, no. 01 (2026). https://doi.org/https://doi.org/10.55041/ijsmt.v2i2.061.
References
. [1] K. Gawhankar, A. Deorukhkar, A. Miniyar, H. Kapure and B. Ivin, "NLP-Driven ML for Resume Information Extraction," 2024 IEEE 9th International Conference for Convergence in Technology (I2CT), Pune, India, 2024, pp. 1-6, doi: 10.1109/I2CT61223.2024.10543861.
[2] B. Nisha, V. Manobharathi, B. Jeyarajanandhini and G. Sivakamasundari, "HR Tech Analyst: Automated Resume Parsing and Ranking System through Natural Language Processing," 2023 2nd International Conference on Automation, Computing and Renewable Systems (ICACRS), Pudukkottai, India, 2023, pp. 1681-1686, doi: 10.1109/ICACRS58579.2023.10404426.
[3] T. G. Sougandh, S. S. K, N. S. Reddy and M. Belwal, "Automated Resume Parsing: A Natural Language Processing Approach," 2023 7th International Conference on Computation System and Information Technology for Sustainable Solutions (CSITSS), Bangalore, India, 2023, pp. 1-6, doi: 10.1109/CSITSS60515.2023.10334236.
[4] J. Zhang, X. Tan, J. Liu and Z. Liu, "Research on Named Entity Recognition Models for Cybersecurity," 2024 2nd International Conference on Signal Processing and Intelligent Computing (SPIC), Guangzhou, China, 2024, pp. 232-237, doi: 10.1109/SPIC62469.2024.10691446.
[5] W. Fulun and Z. Yonghua, "BERT-based Named Entity Recognition Method for Chinese Recipe Text," 2021 IEEE 2nd International Conference on Big Data, Artificial Intelligence and Internet of Things Engineering (ICBAIE), Nanchang, China, 2021, pp. 543-547, doi: 10.1109/ICBAIE52039.2021.9390072.
[6] K. S, P. S. M, P. C and M. K, "Enhancing Named Entity Recognition using Deep Learning Approaches," 2024 5th International Conference on Electronics and Sustainable Communication Systems (ICESC), Coimbatore, India, 2024, pp. 1733-1737, doi: 10.1109/ICESC60852.2024.10690015.
[7] V. Khedkar, D. Desai, S. K. Tidke, C. Fernandes and M. R, "Chemical Named Entity Recognition for Ovarian Cancer’s Drug Discovery," 2022 International Conference on Decision Aid Sciences and Applications (DASA), Chiangrai, Thailand, 2022, pp. 884-889, doi: 10.1109/DASA54658.2022.9765001.
[8] Z. Liu, K. Jiang, Z. Liu and T. Qin, "A Cybersecurity Named Entity Recognition Model Based on Active Learning and Self-learning," 2024 36th Chinese Control and Decision Conference (CCDC), Xi'an, China, 2024, pp. 4505-4510, doi: 10.1109/CCDC62350.2024.10587887.
[9] N. Laosen, K. Laosen and T. Paklao, "Named Entity Recognition for Thai Historical Data," 2024 21st International Joint Conference on Computer Science and Software Engineering (JCSSE), Phuket, Thailand, 2024, pp. 528-533, doi: 10.1109/JCSSE61278.2024.10613644.
[2] B. Nisha, V. Manobharathi, B. Jeyarajanandhini and G. Sivakamasundari, "HR Tech Analyst: Automated Resume Parsing and Ranking System through Natural Language Processing," 2023 2nd International Conference on Automation, Computing and Renewable Systems (ICACRS), Pudukkottai, India, 2023, pp. 1681-1686, doi: 10.1109/ICACRS58579.2023.10404426.
[3] T. G. Sougandh, S. S. K, N. S. Reddy and M. Belwal, "Automated Resume Parsing: A Natural Language Processing Approach," 2023 7th International Conference on Computation System and Information Technology for Sustainable Solutions (CSITSS), Bangalore, India, 2023, pp. 1-6, doi: 10.1109/CSITSS60515.2023.10334236.
[4] J. Zhang, X. Tan, J. Liu and Z. Liu, "Research on Named Entity Recognition Models for Cybersecurity," 2024 2nd International Conference on Signal Processing and Intelligent Computing (SPIC), Guangzhou, China, 2024, pp. 232-237, doi: 10.1109/SPIC62469.2024.10691446.
[5] W. Fulun and Z. Yonghua, "BERT-based Named Entity Recognition Method for Chinese Recipe Text," 2021 IEEE 2nd International Conference on Big Data, Artificial Intelligence and Internet of Things Engineering (ICBAIE), Nanchang, China, 2021, pp. 543-547, doi: 10.1109/ICBAIE52039.2021.9390072.
[6] K. S, P. S. M, P. C and M. K, "Enhancing Named Entity Recognition using Deep Learning Approaches," 2024 5th International Conference on Electronics and Sustainable Communication Systems (ICESC), Coimbatore, India, 2024, pp. 1733-1737, doi: 10.1109/ICESC60852.2024.10690015.
[7] V. Khedkar, D. Desai, S. K. Tidke, C. Fernandes and M. R, "Chemical Named Entity Recognition for Ovarian Cancer’s Drug Discovery," 2022 International Conference on Decision Aid Sciences and Applications (DASA), Chiangrai, Thailand, 2022, pp. 884-889, doi: 10.1109/DASA54658.2022.9765001.
[8] Z. Liu, K. Jiang, Z. Liu and T. Qin, "A Cybersecurity Named Entity Recognition Model Based on Active Learning and Self-learning," 2024 36th Chinese Control and Decision Conference (CCDC), Xi'an, China, 2024, pp. 4505-4510, doi: 10.1109/CCDC62350.2024.10587887.
[9] N. Laosen, K. Laosen and T. Paklao, "Named Entity Recognition for Thai Historical Data," 2024 21st International Joint Conference on Computer Science and Software Engineering (JCSSE), Phuket, Thailand, 2024, pp. 528-533, doi: 10.1109/JCSSE61278.2024.10613644.
Ethics and Compliance
✓ All ethical standards met
This article has undergone plagiarism screening and double-blind peer review. Editorial policies have been followed. Authors retain copyright under CC BY-NC 4.0 license. The research complies with ethical standards and institutional guidelines.
Indexed In
Similar Articles
A Proactive Approach in Detection Of Pneumonia Disease using VGG16
(2026)
DOI: 10.55041/ijsmt.v2i2.041
Veriface AI: A Deep Learning-Based Approach to Deepfake Detection for Image Authenticity Validation
(2026)
DOI: 10.55041/ijsmt.v2i2.050
The Odissi Classical Music Tradition:Gitgovinda to Padabali
(2026)
DOI: 10.55041/ijsmt.v2i2.010
Previous Article
