Abstract
The flood of resumes in today’s talent acquisition landscape makes it difficult for hiring managers and recruiters to find qualified applicants quickly. This study presents an innovative method to integrate artificial intelligence techniques into a custom application to expedite the resume screening process. The program increases the effectiveness of recruitment workflows by a webapp that can be created to take inputs in form of pdf or csv format from the user and screens the resume. The development of a prototype application is the first step in the research methodology. It is then thoroughly verified and analyzed.
References
Jie Chen, Chunxia Zhang and Zhendong Niu, "A Two- Step Resume Information Extraction Algorithm", Mathematical Problems in Engineering, vol. 2018, pp. 8, 2018.
Kajal Kumari, Resume Screening with Natural Language Processing in Python, June 2021.
T Schmitt, P Caillou and M Sebag, "Matching Jobs and Resumes: a Deep Collaborative Filtering Task", Proc. of the 2nd Global Conf. on Artificial Intelligence, pp. 1-14, 2016.
C. Tapsai, "Information Processing and Retrieval from CSV File by Natural Language", 2018 IEEE 3rd International Conference on Communication and Information Systems (ICCIS), pp. 212-216, 2018.
Abdul Wahab and M Nachappa, "Resume Parser with Natural Language Processing", International Research Journal of Engineering and Technology, vol. 09, no. 03, 2022.
Tim Zimmermann, Leo Kotschenreuther and Karsten Schmidt, "Data-driven HR Resume Analysis Based on Natural Language Processing and Machine Learning", arXiv, 2016.
J Chen, Z Niu and H Fu, "A Novel Knowledge Extraction Framework for Resumes Based on Text Classifier", Proceedings of the International Conference on Web-Age Information Management, pp. 540-543, 2015
A. Zaroor, M. Maree and M. Sabha, "A Hybrid Approach to Conceptual Classification and Ranking of Resumes and Their Corresponding Job Posts" in IDT 2017. Smart Innovation Systems and Technologies, Cham:Springer, vol. 72, 2017
Arvind Sinha, Amir Khusru and Ashwani Kumar, "Resume Screening Using Natural Language Processing and Machine Learning: A Systematic Review", Machine Learning and Information Processing Advances in Intelligent Systems and Computing, vol. 1311, 2021.
Yasunobu Kinoa, Hiroshi Kurokia, Tomomi Machidab, Norio Furuyab and Kanako Takano, "Text Analysis for Job Matching Quality Improvement", International Conference on Knowledge Based and Intelligent Information and Engineering Systems Procedia Computer Science, vol. 112, pp. 1523-1530, 2017.
Pradeep Roy, Sarabjeet Chowdhary and Rocky Bhatia, "A Machine Learning approach for automation of Resume Recommendation system", Procedia Computer Science, vol. 167, pp. 2318-2327, 2020.
P. K. Pushp and M. M. Srivastava, "Train once test anywhere: Zero-shot learning for text classification", CoRR, 2017.
A. Eriguchi, M. Johnson, O. Firat, Hideto Kazawa and Wolfgang Macherey, "Zero-Shot Cross-lingual Classification Using Multilingual Neural Machine Translation", CoRR, 2018.
J. Zhang, P. Lertvittayakumjorn and Y. Guo, "Integrating semantic knowledge to tackle zero-shot text classification", NAACL-HLT (2019), pp. 1031-1040, 2019.
S. Antol, C. Zitnick and D. Parikh, "Zero-shot learning via visual abstraction", Proceedings of ECCV, 2014.