Determining a Proper Text Similarity Approach for Resume Parsing Process in a Digitized HR Software

Year 2022, , 371 - 378, 26.12.2022

Yusuf Özçevik , Fatih Yücalar , Murat Demircioğlu

https://doi.org/10.18466/cbayarfbe.1049845

Abstract

Resume parsing is one of the costly phases of a recruitment process. This phase has been alleviated in digitized human resources recently by using text processing approaches between a job advertisement content and resume of applicants. For this purpose, performing a text similarity calculation is one of the most commonly used approaches. However, there are lots of similarity calculation models and most of them are not targeted a recruitment process. Moreover, a subjective assessment of such approaches is required to provide a proper text processing in such a specific problem domain. Thus, in this paper, we offer to evaluate different similarity score calculation approaches through a recruitment case study with the help of a statistical assessment. For this purpose, a computer-aided resume evaluator on a set of resumes is proposed, a human evaluation on the same set of resumes is performed by the professions and the correlation between the outcomes is sought out. As a conclusion, a discussion among different similarity score calculation approaches available for resume processing is presented to find out a proper computer-aided resume evaluator for digitized human resources.

Keywords

Machine Learning based Recruitment, Digitized Human Resources, Text Comparison, Natural Language Processing

References

• Rąb-Kettler, K, Lehnervp, B. Recruitment in the Times of Machine Learning. In: Management Systems in Production Engineering, Sciendo, 2019, pp 105-109.
• Zhao, Y, Hryniewicki, M, K, Cheng F., Fu B., Zhu X. Employee Turnover Prediction with Machine Learning: A Reliable Approach. In: Arai K., Kapoor S., Bhatia R. (eds) Intelligent Systems and Applications, Springer International Publishing, 2018, pp 737–758.
• Connelly, C, E, Fieseler, C, Černe, M, Giessner, S, R, Wong, S, I. 2021. Working in the digitized economy: HRM theory & practice. Human Resource Management Review, 31(1), 100762.
• Zhu, H, 2021. H. Research on Human Resource Recommendation Algorithm Based on Machine Learning. Scientific Programming, pp 2021.
• Pessach, D, Singer, G, Avrahami, D, Ben-Gal, H, C, Shmueli, E, Ben-Gal, I. 2020. Employees recruitment: A prescriptive analytics approach via machine learning and mathematical programming. Decision Support Systems, 134, 113290.
• Javed, F, Luo, Q, McNair, M, Jacob, F, Zhao, M, Kang, T. Carotene: A Job Title Classification System for the Online Recruitment Domain, IEEE First International Conference on Big Data Computing Service and Applications, 2015, pp. 286-293.
• Mujtaba, D, Mahapatra, N. Ethical Considerations in AI-Based Recruitment, IEEE International Symposium on Technology and Society (ISTAS), 2019, pp. 1-7.
• Bandyopadhyay, S, Dutta, S. 2020. Fake Job Recruitment Detection Using Machine Learning Approach. International Journal of Engineering Trends and Technology, vol 68.
• Reddy, D, J, M, Regella, S, Seelam, S. Recruitment Prediction using Machine Learning, International Conference on Computing, Communication and Security (ICCCS), 2020, pp 1-4.
• Gomaa, W, Fahmy, A. 2013. A Survey of Text Similarity Approaches. International journal of Computer Applications, vol 68.
• Paul, P. Efficient Graph-Based Document Similarity. In: The Semantic Web. Latest Advances and New Domains, Springer International Publishing, 2016, pp 334–349.
• Wang, J, Dong, Y. 2020. Measurement of Text Similarity: A Survey. Information, vol 11(9).
• Farouk, M. 2019. Measuring Sentences Similarity: A Survey. Indian Journal of Science and Technology, 12(25), 1–11.
• Rao, J. Bridging the Gap between Relevance Matching and Semantic Matching for Short Text Similarity Modeling, In Proceedings of the Conference on Empirical Methods in Natural Language Processing and the International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), 2019, pp 5370–5381.
• Yujian, L, Bo, L. 2007. A Normalized Levenshtein Distance Metric. IEEE Transactions on Pattern Analysis and Machine Intelligence, vol 29 (6), pp 1091-1095.
• Dreßler, K, Ngomo, A, N. 2017. On the Efficient Execution of Bounded Jaro-Winkler Distances. Semantic Web, vol 8, pp 185-196..
• Del, M, Angeles, M, García-Ugalde, F, Valencia, R, Nava, A. Analysis of String Comparison Methods During De-Duplication Process. International Conference on Advances in Databases, Knowledge, and Data Applications, Rome, Italy, 2015, pp 57-62.
• Bakkelund, D. 2009. An LCS-based string metric. Olso, Norway: University of Oslo.
• Kondrak, G. N-Gram Similarity and Distance. In String Processing and Information Retrieval, Springer Berlin Heidelberg, 2005, pp 115–126.
• Implementation of Cosine Similarity to Calculate Text Relevance between Two Documents. Journal of Physics: Conference Series, vol 978.
• Bag, S, Kumar, S, K, Tiwari, M, K. 2019. An efficient recommendation generation using relevant Jaccard similarity. Information Sciences, vol 483, pp 53-64.
• Salim, D, Perdana, N, J, Mulyawan, B. 2020. Application of the case based reasoning & sorensen-dice coefficient method for fitness exercise program. IOP Conference Series: Materials Science and Engineering, vol 1007(1), pp 012188.
• Kalbaliyev, S. Text Similarity Detection Using Machine Learning Algorithms with Character-Based Similarity Measures. In Digital Interaction and Machine Intelligence, Springer International Publishing, 2021, pp 11–19.
• Zar, J.H. Spearman Rank Correlation: Overview. In: Wiley StatsRef: Statistics Reference Online, 2014.