Adaptive Employee Profile Classification for Resource Planning Tool

Gonzalez, Tere; Santos, Pano; Orozco, Fernando; Alcaraz, Mildreth; Zaldivar, Victor; Obeso, Alberto De; García, Alan

doi:10.1109/srii.2012.67

Cited by 10 publications

(3 citation statements)

References 8 publications

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“…Several studies have proposed the Machine Learning based system for Human Resource Management and recruiting processes. For instance, the study [20] designed the approach for Resume ranking that uses that layered information retrieval framework to parse the resumes. The goal of this study was to help recruiters to find out the relevant job applicant for a job opening.…”

Section: Related Workmentioning

confidence: 99%

Resume Classification System using Natural Language Processing and Machine Learning Techniques

Ali

Mughal

Khan

et al. 2022

murjet

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The selection of a suitable job applicant from the pool of thousands applications is often daunting job for an employer. The categorization of job applications submitted in form of Resumes against available vacancy(s) takes significant time and efforts of an employer. Thus, Resume Classification System (RCS) using the Natural Language Processing (NLP) and Machine Learning (ML) techniques could automate this tedious process. Moreover, the automation of this process can significantly expedite and transparent the applicants’ screening process with mere human involvement. This experimental study presents an automated NLP and ML-based RCS that classifies the Resumes according to job categories with performance guarantees. This study employs various ML algorithms and NLP techniques to measure the accuracy of RCS and proposes a solution with better accuracy and reliability in different settings. To demonstrate the significance of NLP and ML techniques for RCS, the extracted features were evaluated on nine ML classification models namely Support Vector Machine - SVM (Linear, SGD, SVC and NuSVC), Naïve Bayes (Bernoulli, Multinomial & Gaussian), K-Nearest Neighbor (KNN), and Logistic Regression (LR). The Term-Frequency-Inverse-Document-Frequency (TF-IDF) feature representation scheme was proved suitable for RCS. The developed models were evaluated using the Confusion Matrix, F-Score, Recall, Precision, and overall Accuracy. The experimental results indicate that using the One-Vs-Rest-Classification strategy for this multi-class Resume classification task, the SVM class of Machine Learning classifiers performed better on the study dataset of over nine hundred sixty plus parsed resumes with more than 96% accuracy. The promising results suggest that NLP and ML techniques employed in this study could be used for developing an efficient RCS.

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Section: Related Workmentioning

confidence: 99%

Resume Classification System using Natural Language Processing and Machine Learning Techniques

Ali

Mughal

Khan

et al. 2022

murjet

View full text Add to dashboard Cite

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“…HP also built a system to solve the similar problem, which is introduced in Gonzalez et al's paper [17]. The system also pays a lot of attention to information extraction.…”

Section: Information Extraction In Job Recommender Systemmentioning

confidence: 99%

RésuMatcher: A personalized résumé-job matching system

Guo

Alamudun

Hammond

2016

Expert Systems with Applications

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“…At present, personnel officers use and, at the same time, refine and seek techniques allowing them to formulate profiles of their employees on various positions and enable them to draw up profiles of applicants for various jobs and compare them (e.g. Gonzalez, Santos, & Orozco, 2012, Singh, Rose, Visweswariah, & Chenthamarakshan, 2010. The identification of heroes admired by the applicants can become one of such techniques.…”

Section: Introduction and Theoretical Backgroundmentioning

confidence: 99%

Can HRM Utilize Hero Analyses in Identifying Employees’ Profiles? The Case of the Slovak and Czech Republics

Šajgalíková

Copuš

2020

JEECAR

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Finding feasible ways to predict the behaviour of organizations’ current and future employees is one of the most crucial HRM roles. The identification of heroes is a potential and innovative technique. The objective of the study is to identify heroes recognized by university students (to-be employees) and the values the students attach to them, as the grounds for establishing an employee profile useful in recruitment and selection processes. Simultaneously, the study pays attention to the respondents’ cultural context, namely the one of national culture (students of Slovak and Czech universities). The sample employee profiles open new opportunities for further exploration of the approach on the academic level as well as in the HRM practice. The differences identified in applicants’ hero categories significantly enhance understanding of their behavioural tendencies. The findings open several questions about the role of companies as well as educational systems in future employees’ value system evolution.

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Adaptive Employee Profile Classification for Resource Planning Tool

Cited by 10 publications

References 8 publications

Resume Classification System using Natural Language Processing and Machine Learning Techniques

Resume Classification System using Natural Language Processing and Machine Learning Techniques

RésuMatcher: A personalized résumé-job matching system

Can HRM Utilize Hero Analyses in Identifying Employees’ Profiles? The Case of the Slovak and Czech Republics

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