Utilizing Named Entity Recognition for Web-Based Resume Scoring
Utilizing Named Entity Recognition for Web-Based Resume Scoring |
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| © 2024 by IJETT Journal | ||
| Volume-72 Issue-7 |
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| Year of Publication : 2024 | ||
| Author : Ivic Jan A. Biol, Cristopher C. Abalorio |
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| DOI : 10.14445/22315381/IJETT-V72I7P142 | ||
How to Cite?
Ivic Jan A. Biol, Cristopher C. Abalorio, "Utilizing Named Entity Recognition for Web-Based Resume Scoring," International Journal of Engineering Trends and Technology, vol. 72, no. 7, pp. 381-387, 2024. Crossref, https://doi.org/10.14445/22315381/IJETT-V72I7P142
Abstract
The overwhelming number of job applicants received by companies has made resume evaluation a time-consuming task for recruiters. Online recruitment platforms have emerged as a solution for automating the matching of job openings with suitable resumes. This study analyzes the use of Named Entity Recognition (NER) to automate the evaluation of resumes in the hiring process. NER was employed as a resume scorer to extract relevant skills, education, and work experience from resumes. By identifying named entities, such as programming languages, education institutions, and job titles, the system efficiently assessed candidate qualifications and matched them to job requirements. This study utilized a dataset of 1,014 annotated resumes, and the RoBERTa NER model was fine-tuned using spacy transformers. In addition, the NER model for job descriptions was trained using a dataset of 200 job descriptions. The results demonstrated improvements in model performance over training epochs, with increased precision, recall, and F1 scores. This study highlights the potential of web-based resume scorers in automating resume evaluations and suggests directions for future research in this area.
Keywords
Natural Language Processing, Named Entity Recognition, Resume scorer, RoBERTa.
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