Abstract
One of the current job recruiter’s biggest challenges is to filter the right candidate’s resume over the pool of resumes. For a single job post, many times more than thousands of applicants send their resumes. However, many of them are not suitable for the offered job. Manually filtering the right candidate’s resume is not feasible from the pool; hence, an automated system may help pick the selective candidate’s resume by applying natural language processing. This research suggested a machine learning-based automated resume classification model which classifies the resume into different categories based on their content. The experiment is done with the dataset consisting of ten categories of resumes. The outcomes of the proposed model achieve satisfactory classification reports in terms of precision, recall and F1-score with bi-gram model.
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Roy, P.K., Singh, S.K., Das, T.K., Tripathy, A.K. (2022). Automated Resume Classification Using Machine Learning. In: Rout, R.R., Ghosh, S.K., Jana, P.K., Tripathy, A.K., Sahoo, J.P., Li, KC. (eds) Advances in Distributed Computing and Machine Learning. Lecture Notes in Networks and Systems, vol 427. Springer, Singapore. https://doi.org/10.1007/978-981-19-1018-0_26
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DOI: https://doi.org/10.1007/978-981-19-1018-0_26
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