Job description parsing with explainable transformer based ensemble models to extract the technical and non-technical skills
The rapid digitization of the economy is transforming the job market, creating new roles and reshaping existing ones. As skill requirements evolve, identifying essential competencies becomes increasingly critical. This paper introduces a novel ensemble model that combines traditional and transformer...
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| Format: | Article |
| Language: | English |
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Elsevier
2024-12-01
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| Series: | Natural Language Processing Journal |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S2949719124000505 |
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| author | Abbas Akkasi |
| author_facet | Abbas Akkasi |
| author_sort | Abbas Akkasi |
| collection | DOAJ |
| description | The rapid digitization of the economy is transforming the job market, creating new roles and reshaping existing ones. As skill requirements evolve, identifying essential competencies becomes increasingly critical. This paper introduces a novel ensemble model that combines traditional and transformer-based neural networks to extract both technical and non-technical skills from job descriptions. A substantial dataset of job descriptions from reputable platforms was meticulously annotated for 22 IT roles. The model demonstrated superior performance in extracting both non-technical (67% F-score) and technical skills (72% F-score) compared to conventional CRF and hybrid deep learning models. Specifically, the proposed model outperformed these baselines by an average margin of 10% and 6%, respectively, for non-technical skills, and 29% and 6.8% for technical skills. A 5 × 2cv paired t-test confirmed the statistical significance of these improvements. In addition, to enhance model interpretability, Local Interpretable Model-Agnostic Explanations (LIME) were employed in the experiments. |
| format | Article |
| id | doaj-art-80be188df2d34b718ae8555f0b0b34bd |
| institution | Kabale University |
| issn | 2949-7191 |
| language | English |
| publishDate | 2024-12-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Natural Language Processing Journal |
| spelling | doaj-art-80be188df2d34b718ae8555f0b0b34bd2024-12-14T06:34:31ZengElsevierNatural Language Processing Journal2949-71912024-12-019100102Job description parsing with explainable transformer based ensemble models to extract the technical and non-technical skillsAbbas Akkasi0School of Computer Science, Carleton University, Ottawa, Ontario, CanadaThe rapid digitization of the economy is transforming the job market, creating new roles and reshaping existing ones. As skill requirements evolve, identifying essential competencies becomes increasingly critical. This paper introduces a novel ensemble model that combines traditional and transformer-based neural networks to extract both technical and non-technical skills from job descriptions. A substantial dataset of job descriptions from reputable platforms was meticulously annotated for 22 IT roles. The model demonstrated superior performance in extracting both non-technical (67% F-score) and technical skills (72% F-score) compared to conventional CRF and hybrid deep learning models. Specifically, the proposed model outperformed these baselines by an average margin of 10% and 6%, respectively, for non-technical skills, and 29% and 6.8% for technical skills. A 5 × 2cv paired t-test confirmed the statistical significance of these improvements. In addition, to enhance model interpretability, Local Interpretable Model-Agnostic Explanations (LIME) were employed in the experiments.http://www.sciencedirect.com/science/article/pii/S2949719124000505Ensemble learningConvolutional Neural Network (CNN)Bidirectional Long-Short Term Memory (biLSTM)Conditional Random Fields(CRF)Transformer |
| spellingShingle | Abbas Akkasi Job description parsing with explainable transformer based ensemble models to extract the technical and non-technical skills Natural Language Processing Journal Ensemble learning Convolutional Neural Network (CNN) Bidirectional Long-Short Term Memory (biLSTM) Conditional Random Fields(CRF) Transformer |
| title | Job description parsing with explainable transformer based ensemble models to extract the technical and non-technical skills |
| title_full | Job description parsing with explainable transformer based ensemble models to extract the technical and non-technical skills |
| title_fullStr | Job description parsing with explainable transformer based ensemble models to extract the technical and non-technical skills |
| title_full_unstemmed | Job description parsing with explainable transformer based ensemble models to extract the technical and non-technical skills |
| title_short | Job description parsing with explainable transformer based ensemble models to extract the technical and non-technical skills |
| title_sort | job description parsing with explainable transformer based ensemble models to extract the technical and non technical skills |
| topic | Ensemble learning Convolutional Neural Network (CNN) Bidirectional Long-Short Term Memory (biLSTM) Conditional Random Fields(CRF) Transformer |
| url | http://www.sciencedirect.com/science/article/pii/S2949719124000505 |
| work_keys_str_mv | AT abbasakkasi jobdescriptionparsingwithexplainabletransformerbasedensemblemodelstoextractthetechnicalandnontechnicalskills |