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Skills play a central role in the job market and many human resources (HR) processes.
Scikit-learn: Machine learning in Python,
F. Pedregosa, G. Varoquaux, A. Gramfort, V. Michel, B. Thirion, O. Grisel, M. Blondel, P. Prettenhofer, R. Weiss, V. Dubourg, J. Vanderplas, A. Passos, D. Cournapeau, M. Brucher, M. Perrot, E. Duchesnay, · 2011
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Analysis of learning from positive and unlabeled data,
M. C. Du Plessis, G. Niu, M. Sugiyama, · 2014
Earlier work this paper cites.
Skill: A system for skill identification and normalization,
M. Zhao, F. Javed, F. Jacob, M. McNair, · 2015
Earlier work this paper cites.
Knowledge base population using semantic label propagation,
L. Sterckx, T. Demeester, J. Deleu, C. Develder, · 2016
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European skills, competences, qualifications and occupations,
ESCO, · 2017
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Learning representations for soft skill matching,
L. Sayfullina, E. Malmi, J. Kannala, · 2018
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Representation of job-skill in artificial intelligence with knowledge graph analysis,
S. Jia, X. Liu, P. Zhao, C. Liu, L. Sun, T. Peng, · 2018
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Extreme multi-label legal text classification: A case study in EU legislation,
I. Chalkidis, M. Fergadiotis, P. Malakasiotis, N. Aletras, I. Androutsopoulos, · 2019
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Benchmarking zero-shot text classification: Datasets, evaluation and entailment approach,
W. Yin, J. Hay, D. Roth, · 2019
Cited alongside, same era.
Roberta: A robustly optimized bert pretraining approach,
Y. Liu, M. Ott, N. Goyal, J. Du, M. Joshi, D. Chen, O. Levy, M. Lewis, L. Zettlemoyer, V. Stoyanov, · 2019
Cited alongside, same era.
Sentence-bert: Sentence embeddings using siamese bert-networks,
N. Reimers, I. Gurevych, · 2019
Cited alongside, same era.
Retrieving skills from job descriptions: A language model based extreme multi-label classification framework,
A. Bhola, K. Halder, A. Prasad, M.-Y. Kan, · 2020
Cited alongside, same era.
DataOps for societal intelligence: A data pipeline for labor market skills extraction and matching,
A survey on skill identification from online job ads,
I. Khaouja, I. Kassou, M. Ghogho, · 2021
Later among the works it cites.
Extreme zero-shot learning for extreme text classification,
Y. Xiong, W.-C. Chang, C.-J. Hsieh, H.-F. Yu, I. Dhillon, · 2021
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Multi-label learning from single positive labels,
E. Cole, O. Mac Aodha, T. Lorieul, P. Perona, D. Morris, N. Jojic, · 2021
Later among the works it cites.
Jobbert: Understanding job titles through skills,
J.-J. Decorte, J. Van Hautte, T. Demeester, C. Develder, · 2021
Later among the works it cites.
“FIJO”: a French insurance soft skill detection dataset,
D. Beauchemin, J. Laumonier, Y. L. Ster, M. Yassine, · 2022
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D. A. Tamburri, W.-J. Van Den Heuvel, M. Garriga, · 2020
Cited alongside, same era.
RobBERT: a Dutch Roberta-based language model,
P. Delobelle, T. Winters, B. Berendt, · 2020
Cited alongside, same era.
Multi-label few/zero-shot learning with knowledge aggregated from multiple label graphs,
J. Lu, L. Du, M. Liu, J. Dipnall, · 2020
Cited alongside, same era.
Contrastive learning with hard negative samples,
J. Robinson, C.-Y. Chuang, S. Sra, S. Jegelka, · 2020
Cited alongside, same era.
Skillspan: Hard and soft skill extraction from English job postings,
M. Zhang, K. N. Jensen, S. D. Sonniks, B. Plank,
Cited in the paper.
M. Zhang, K. N. Jensen, B. Plank,
Cited in the paper.
Using RobBERT and eXtreme multi-label classification to extract implicit and explicit skills from Dutch job descriptions (2022)
N. Vermeer, V. Provatorova, D. Graus, T. Rajapakse, S. Mesbah, · 2022
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Acknowledging the unknown for multi-label learning with single positive labels,
D. Zhou, P. Chen, Q. Wang, G. Chen, P.-A. Heng, · 2022
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