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Occupational profiling driven by online job advertisements: Taking the data analysis and processing engineering technicians as an example
The occupational profiling system driven by the traditional survey method has some shortcomings such as lag in updating, time consumption and laborious revision. It is necessary to refine and improve the traditional occupational portrait system through dynamic occupational information. Under the cir...
Autores principales: | , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Public Library of Science
2021
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8219172/ https://www.ncbi.nlm.nih.gov/pubmed/34157028 http://dx.doi.org/10.1371/journal.pone.0253308 |
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author | Cao, Lina Zhang, Jian Ge, Xinquan Chen, Jindong |
author_facet | Cao, Lina Zhang, Jian Ge, Xinquan Chen, Jindong |
author_sort | Cao, Lina |
collection | PubMed |
description | The occupational profiling system driven by the traditional survey method has some shortcomings such as lag in updating, time consumption and laborious revision. It is necessary to refine and improve the traditional occupational portrait system through dynamic occupational information. Under the circumstances of big data, this paper showed the feasibility of vocational portraits driven by job advertisements with data analysis and processing engineering technicians (DAPET) as an example. First, according to the description of occupation in the Chinese Occupation Classification Grand Dictionary, a text similarity algorithm was used to preliminarily choose recruitment data with high similarity. Second, Convolutional Neural Networks for Sentence Classification (TextCNN) was used to further classify the preliminary corpus to obtain a precise occupational dataset. Third, the specialty and skill were taken as named entities that were automatically extracted by the named entity recognition technology. Finally, putting the extracted entities into the occupational dataset, the occupation characteristics of multiple dimensions were depicted to form a profile of the vocation. |
format | Online Article Text |
id | pubmed-8219172 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2021 |
publisher | Public Library of Science |
record_format | MEDLINE/PubMed |
spelling | pubmed-82191722021-07-07 Occupational profiling driven by online job advertisements: Taking the data analysis and processing engineering technicians as an example Cao, Lina Zhang, Jian Ge, Xinquan Chen, Jindong PLoS One Research Article The occupational profiling system driven by the traditional survey method has some shortcomings such as lag in updating, time consumption and laborious revision. It is necessary to refine and improve the traditional occupational portrait system through dynamic occupational information. Under the circumstances of big data, this paper showed the feasibility of vocational portraits driven by job advertisements with data analysis and processing engineering technicians (DAPET) as an example. First, according to the description of occupation in the Chinese Occupation Classification Grand Dictionary, a text similarity algorithm was used to preliminarily choose recruitment data with high similarity. Second, Convolutional Neural Networks for Sentence Classification (TextCNN) was used to further classify the preliminary corpus to obtain a precise occupational dataset. Third, the specialty and skill were taken as named entities that were automatically extracted by the named entity recognition technology. Finally, putting the extracted entities into the occupational dataset, the occupation characteristics of multiple dimensions were depicted to form a profile of the vocation. Public Library of Science 2021-06-22 /pmc/articles/PMC8219172/ /pubmed/34157028 http://dx.doi.org/10.1371/journal.pone.0253308 Text en © 2021 Cao et al https://creativecommons.org/licenses/by/4.0/This is an open access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/) , which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. |
spellingShingle | Research Article Cao, Lina Zhang, Jian Ge, Xinquan Chen, Jindong Occupational profiling driven by online job advertisements: Taking the data analysis and processing engineering technicians as an example |
title | Occupational profiling driven by online job advertisements: Taking the data analysis and processing engineering technicians as an example |
title_full | Occupational profiling driven by online job advertisements: Taking the data analysis and processing engineering technicians as an example |
title_fullStr | Occupational profiling driven by online job advertisements: Taking the data analysis and processing engineering technicians as an example |
title_full_unstemmed | Occupational profiling driven by online job advertisements: Taking the data analysis and processing engineering technicians as an example |
title_short | Occupational profiling driven by online job advertisements: Taking the data analysis and processing engineering technicians as an example |
title_sort | occupational profiling driven by online job advertisements: taking the data analysis and processing engineering technicians as an example |
topic | Research Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8219172/ https://www.ncbi.nlm.nih.gov/pubmed/34157028 http://dx.doi.org/10.1371/journal.pone.0253308 |
work_keys_str_mv | AT caolina occupationalprofilingdrivenbyonlinejobadvertisementstakingthedataanalysisandprocessingengineeringtechniciansasanexample AT zhangjian occupationalprofilingdrivenbyonlinejobadvertisementstakingthedataanalysisandprocessingengineeringtechniciansasanexample AT gexinquan occupationalprofilingdrivenbyonlinejobadvertisementstakingthedataanalysisandprocessingengineeringtechniciansasanexample AT chenjindong occupationalprofilingdrivenbyonlinejobadvertisementstakingthedataanalysisandprocessingengineeringtechniciansasanexample |