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Journals / Alphanumeric Journal / 2024 / Volume: 12 Issue: 2

Resume Screening With Natural Language Processing (NLP)

Mehtap Saatci(ABDULLAH GUL UNIVERSITY, FACULTY OF ENGINEERING)0009-0007-1170-6526
Rukiye Kaya(ABDULLAH GUL UNIVERSITY, FACULTY OF ENGINEERING)0009-0003-5881-0305
Ramazan Ünlü(ABDULLAH GUL UNIVERSITY, FACULTY OF ENGINEERING)0000-0002-1201-195X
Pages
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Publication Date
2024-12-31
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AI Summary & Key Takeaways

AI Insight
⚡ TL;DR(Too Long; Didn't Read)

Overall, this study demonstrates the transformative potential of NLP in optimizing the resume screening process by providing a scalable, efficient, and unbiased solution for large organizations.

📌 3 Key Takeaways

🎯
Objective & Hypothesis:This study addresses the challenges employers face in screening the large number of resumes received for job positions.
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Key Findings & Data:We aim to ensure fair evaluation of candidates, reduce bias, and increase the efficiency of the candidate evaluation process by automating the resume screening process.
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Conclusion & Impact:Overall, this study demonstrates the transformative potential of NLP in optimizing the resume screening process by providing a scalable, efficient, and unbiased solution for large organizations.
🔬Design:Research Article
📍Scope:Comprehensive Sample
⚙️Variables:Natural Language Processing (NLP) · Resume Screening · Jaccard Similarity

Abstract

This study addresses the challenges employers face in screening the large number of resumes received for job positions. We aim to ensure fair evaluation of candidates, reduce bias, and increase the efficiency of the candidate evaluation process by automating the resume screening process. The proposed system uses NLP techniques to extract the relevant competencies from resumes, focusing on the key skills required for specific positions. The competency sets taken for the positions were used. A case study was conducted for 123 job positions. The extracted competencies are matched to predefined skill sets associated with various job positions using Jaccard Similarity. This method provides a similarity score that helps rank candidates by comparing the presence or absence of words in the candidate's resume to the required competencies. This NLP-based system offers significant benefits such as saving time and resources, increasing accuracy in candidate selection, and reducing bias by focusing only on competencies. The system's integration with LinkedIn increases its usefulness by allowing seamless import and analysis of resumes. Overall, this study demonstrates the transformative potential of NLP in optimizing the resume screening process by providing a scalable, efficient, and unbiased solution for large organizations.

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Cite This Article

Saatci, M., Kaya, R., Ünlü, R. (2024). Resume Screening With Natural Language Processing (NLP). Alphanumeric Journal, Volume: 12 Issue: 2. https://doi.org/10.17093/alphanumeric.1536577

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References (31)

  1. Alamelu, M., Kumar, D., Sanjana, R., Sree, J., Devi, A., & Kavitha, D. (2021). Resume Validation and Filtration using Natural Language Processing. 2021 10th International Conference on Internet of Everything, Microwave Engineering, Communication and Networks (IEMECON), 1–5. https://doi.org/10.1109/iemecon53809.2021.9689075
  2. Ali, I., Mughal, N., Khan, Z. H., Ahmed, J., & Mujtaba, G. (2022). Resume Classification System using Natural Language Processing and Machine Learning Techniques. Mehran University Research Journal of Engineering and Technology, 41(1), 65–79. https://doi.org/10.22581/muet1982.2201.07
  3. Aminu, H., Yau, B. I., Zambuk, F. U., Nanin, E. R., Abdullahi, A., & Yakubu, I. Z. (2023). Salary Prediction Model using Principal Component Analysis and Deep Neural Network Algorithm. International Journal of Innovative Science and Research Technology, 8(12), 1–11. https://doi.org/10.5281/ZENODO.10629245
  4. Anand, A., & Dubey, M. S. (2022). CV Analysis Using Machine Learning. International Journal for Research in Applied Science and Engineering Technology, 10(5), 1316–1322. https://doi.org/10.22214/ijraset.2022.42295
  5. Aydin, O., Karaarslan, E., & Narin, N. G. (2024). Artificial Intelligence, VR, AR and Metaverse Technologies for Human Resources Management. https://doi.org/10.48550/ARXIV.2406.15383
  6. Bharadwaj, S., Varun, R., Aditya, P. S., Nikhil, M., & Babu, G. C. (2022). Resume Screening using NLP and LSTM. 2022 International Conference on Inventive Computation Technologies (ICICT), 238–241. https://doi.org/10.1109/icict54344.2022.9850889
  7. Cabrera-Diego, L. A., Durette, B., Lafon, M., Torres-Moreno, J.-M., & El-Bèze, M. (2015). How Can We Measure the Similarity Between Résumés of SelectedCandidates for a Job?. International Conference on Data Mining DMIN 2015.
  8. Chou, Y.-C., & Yu, H.-Y. (2020). Based on the application of AI technology in resume analysis and job recommendation. 2020 IEEE International Conference on Computational Electromagnetics (ICCEM), 291–296. https://doi.org/10.1109/ICCEM47450.2020.9219491
  9. Daryani, C., Chhabra, G. S., Patel, H., Chhabra, I. K., & Patel, R. (2020, January). An Automated Resume Screening System Using Natural Language Processing and Similarity. Ethics and Information Technology. https://doi.org/10.26480/etit.02.2020.99.103
  10. FraiJ, J., & László, V. (2021). Literature Review: Artificial Intelligence Impact on the Recruitment Process. International Journal of Engineering and Management Sciences, 6(1), 108–119. https://doi.org/10.21791/ijems.2021.1.10.
  11. Gan, C., Zhang, Q., & Mori, T. (2024). Application of LLM Agents in Recruitment: A Novel Framework for Automated Resume Screening. Journal of Information Processing, 32(0), 881–893. https://doi.org/10.2197/ipsjjip.32.881
  12. Harsha, T. M., Moukthika, G. S., Sai, D. S., Pravallika, M. N. R., Anamalamudi, S., & Enduri, M. (2022). Automated Resume Screener using Natural Language Processing(NLP). 2022 6th International Conference on Trends in Electronics and Informatics (ICOEI), 1772–1777. https://doi.org/10.1109/icoei53556.2022.9777194
  13. Kino, Y., Kuroki, H., Machida, T., Furuya, N., & Takano, K. (2017). Text Analysis for Job Matching Quality Improvement. Procedia Computer Science, 112, 1523–1530. https://doi.org/10.1016/j.procs.2017.08.054
  14. Lad, A., Ghosalkar, S., Bane, B., Pagade, K., & Chaurasia, A. (2022). Machine Learning Based Resume Recommendation System. International Journal of Modern Developments in Engineering and Science, 1(3), 17–20. https://journal.ijmdes.com/ijmdes/article/view/17
  15. Lalitha, B., Kadiyam, S., Kalidindi, R. V., Vemparala, S. M., Yarlagadda, K., & Chekuri, S. V. (2023). Applicant Screening System Using NLP. 2023 International Conference on Innovative Data Communication Technologies and Application (ICIDCA), 379–383. https://doi.org/10.1109/icidca56705.2023.10099953
  16. Li, C., Fisher, E., Thomas, R., Pittard, S., Hertzberg, V., & Choi, J. D. (2020). Competence-Level Prediction and Resume & Job Description Matching Using Context-Aware Transformer Models. Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP), 8456–8466. https://doi.org/10.18653/v1/2020.emnlp-main.679
  17. Li, S., Li, K., & Lu, H. (2023). National Origin Discrimination in Deep-learning-powered Automated Resume Screening. 1–15. https://doi.org/10.48550/ARXIV.2307.08624
  18. Mehboob, M., Ali, M. S., Ul Islam, S., & Sarmad Ali, S. (2022). Evaluating Automatic CV Shortlisting Tool For Job Recruitment Based On Machine Learning Techniques. 2022 Mohammad Ali Jinnah University International Conference on Computing (MAJICC), 1–4. https://doi.org/10.1109/majicc56935.2022.9994112
  19. Mohanty, S., Behera, A., Mishra, S., Alkhayyat, A., Gupta, D., & Sharma, V. (2023). Resumate: A Prototype to Enhance Recruitment Process with NLP based Resume Parsing. 2023 4th International Conference on Intelligent Engineering and Management (ICIEM), 1–6. https://doi.org/10.1109/iciem59379.2023.10166169
  20. Naveed, Z., Nisar, B., Saifullah, D. M., & Iqbal Baig, J. (2024). Resume Ranking Using Natural Language Processing. Journal of Computers and Intelligent Systems, 2(1), 61–66. https://journals.iub.edu.pk/index.php/JCIS/article/view/2806
  21. Pal, R., Shaikh, S., Satpute, S., & Bhagwat, S. (2022). Resume Classification using various Machine Learning Algorithms. ITM Web of Conferences, 44, 3011. https://doi.org/10.1051/itmconf/20224403011
  22. Pant, D., Pokhrel, D., & Poudyal, P. (2022). Automatic Software Engineering Position Resume Screening using Natural Language Processing, Word Matching, Character Positioning, and Regex. 2022 5th International Conference on Advanced Systems and Emergent Technologies (IC_ASET), 44–48. https://doi.org/10.1109/ic\_aset53395.2022.9765916
  23. Pimpalkar, A., Lalwani, A., Chaudhari, R., Inshall, M., Dalwani, M., & Saluja, T. (2023). Job Applications Selection and Identification: Study of Resumes with Natural Language Processing and Machine Learning. 2023 IEEE International Students' Conference on Electrical, Electronics and Computer Science (SCEECS), 1–5. https://doi.org/10.1109/sceecs57921.2023.10063010
  24. Roy, P. K., Chowdhary, S. S., & Bhatia, R. (2020). A Machine Learning approach for automation of Resume Recommendation system. Procedia Computer Science, 167, 2318–2327. https://doi.org/10.1016/j.procs.2020.03.284
  25. Sajid, H., Kanwal, J., Bhatti, S. U. R., Qureshi, S. A., Basharat, A., Hussain, S., & Khan, K. U. (2022). Resume Parsing Framework for E-recruitment. 2022 16th International Conference on Ubiquitous Information Management and Communication (IMCOM), 1–8. https://doi.org/10.1109/imcom53663.2022.9721762
  26. Satheesh, K., Jahnavi, A., Iswarya, L., Ayesha, K., Bhanusekhar, G., & Hanisha, K. (2020). Resume Ranking based on Job Description using SpaCy NER model. International Research Journal of Engineering and Technology, 7(5), 74–77.
  27. Spoorthi, M., Indu Priya, B., Kuppala, M., Karpe, V. S., & Dharavath, D. (2023). Automated Resume Classification System Using Ensemble Learning. 2023 9th International Conference on Advanced
  28. Computing and Communication Systems (ICACCS), 1782–1785. https://doi.org/10.1109/icaccs57279.2023.10112917
  29. Suhas Chavare, D., & Bhaskar Patil, A. (2023). Resume Parsing using Natural Language Processing. GRENZE International Journal of Engineering and Technology, 9(1), 721–726.
  30. Tejaswini, K., Umadevi, V., Kadiwal, S. M., & Revanna, S. (2022). Design and development of machine learning based resume ranking system. Global Transitions Proceedings, 3(2), 371–375. https://doi.org/10.1016/j.gltp.2021.10.002
  31. Trinh, Q., & Dang, T.-T. (2021). Automatic Process Resume in Talent Pool by Applying Natural Language Processing. International Conference on Logistics and Industrial Engineering 2021, 234–240.