Making personnel selection smarter through word embeddings: A graph-based approach

This paper employs techniques and algorithms from the fields of natural language processing, graph representation learning and word embeddings to assist project managers in the task of personnel selection. To do so, our approach initially represents multiple textual documents as a single graph. Then...

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Autores principales:	Nikos Kanakaris, Nikolaos Giarelis, Ilias Siachos, Nikos Karacapilidis
Formato:	article
Lenguaje:	EN
Publicado:	Elsevier 2022
Materias:	Natural language processing Text categorization Graph representation learning Issue management Personnel selection Word embeddings Cybernetics Q300-390 Electronic computers. Computer science QA75.5-76.95
Acceso en línea:	https://doaj.org/article/1373c9dd1f2246fd861ed3619b381643
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spelling	oai:doaj.org-article:1373c9dd1f2246fd861ed3619b3816432021-11-30T04:17:55ZMaking personnel selection smarter through word embeddings: A graph-based approach2666-827010.1016/j.mlwa.2021.100214https://doaj.org/article/1373c9dd1f2246fd861ed3619b3816432022-03-01T00:00:00Zhttp://www.sciencedirect.com/science/article/pii/S2666827021001079https://doaj.org/toc/2666-8270This paper employs techniques and algorithms from the fields of natural language processing, graph representation learning and word embeddings to assist project managers in the task of personnel selection. To do so, our approach initially represents multiple textual documents as a single graph. Then, it computes word embeddings through representation learning on graphs and performs feature selection. Finally, it builds a classification model that is able to estimate how qualified a candidate employee is to work on a given task, taking as input only the descriptions of the tasks and a list of word embeddings. Our approach differs from the existing ones in that it does not require the calculation of key performance indicators or any other form of structured data in order to operate properly. For our experiments, we retrieved data from the Jira issue tracking system of the Apache Software Foundation. The evaluation results show, in most cases, an increase of 0.43% in the accuracy of the proposed classification models when compared against a widely-adopted baseline method, while their validation loss is significantly decreased by 65.54%.Nikos KanakarisNikolaos GiarelisIlias SiachosNikos KaracapilidisElsevierarticleNatural language processingText categorizationGraph representation learningIssue managementPersonnel selectionWord embeddingsCyberneticsQ300-390Electronic computers. Computer scienceQA75.5-76.95ENMachine Learning with Applications, Vol 7, Iss , Pp 100214- (2022)
institution	DOAJ
collection	DOAJ
language	EN
topic	Natural language processing Text categorization Graph representation learning Issue management Personnel selection Word embeddings Cybernetics Q300-390 Electronic computers. Computer science QA75.5-76.95
spellingShingle	Natural language processing Text categorization Graph representation learning Issue management Personnel selection Word embeddings Cybernetics Q300-390 Electronic computers. Computer science QA75.5-76.95 Nikos Kanakaris Nikolaos Giarelis Ilias Siachos Nikos Karacapilidis Making personnel selection smarter through word embeddings: A graph-based approach
description	This paper employs techniques and algorithms from the fields of natural language processing, graph representation learning and word embeddings to assist project managers in the task of personnel selection. To do so, our approach initially represents multiple textual documents as a single graph. Then, it computes word embeddings through representation learning on graphs and performs feature selection. Finally, it builds a classification model that is able to estimate how qualified a candidate employee is to work on a given task, taking as input only the descriptions of the tasks and a list of word embeddings. Our approach differs from the existing ones in that it does not require the calculation of key performance indicators or any other form of structured data in order to operate properly. For our experiments, we retrieved data from the Jira issue tracking system of the Apache Software Foundation. The evaluation results show, in most cases, an increase of 0.43% in the accuracy of the proposed classification models when compared against a widely-adopted baseline method, while their validation loss is significantly decreased by 65.54%.
format	article
author	Nikos Kanakaris Nikolaos Giarelis Ilias Siachos Nikos Karacapilidis
author_facet	Nikos Kanakaris Nikolaos Giarelis Ilias Siachos Nikos Karacapilidis
author_sort	Nikos Kanakaris
title	Making personnel selection smarter through word embeddings: A graph-based approach
title_short	Making personnel selection smarter through word embeddings: A graph-based approach
title_full	Making personnel selection smarter through word embeddings: A graph-based approach
title_fullStr	Making personnel selection smarter through word embeddings: A graph-based approach
title_full_unstemmed	Making personnel selection smarter through word embeddings: A graph-based approach
title_sort	making personnel selection smarter through word embeddings: a graph-based approach
publisher	Elsevier
publishDate	2022
url	https://doaj.org/article/1373c9dd1f2246fd861ed3619b381643
work_keys_str_mv	AT nikoskanakaris makingpersonnelselectionsmarterthroughwordembeddingsagraphbasedapproach AT nikolaosgiarelis makingpersonnelselectionsmarterthroughwordembeddingsagraphbasedapproach AT iliassiachos makingpersonnelselectionsmarterthroughwordembeddingsagraphbasedapproach AT nikoskaracapilidis makingpersonnelselectionsmarterthroughwordembeddingsagraphbasedapproach
_version_	1718406727268827136

Making personnel selection smarter through word embeddings: A graph-based approach

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