@inproceedings{goyal-etal-2023-jobxmlc,
title = "{J}ob{XMLC}: {EX}treme Multi-Label Classification of Job Skills with Graph Neural Networks",
author = "Goyal, Nidhi and
Kalra, Jushaan and
Sharma, Charu and
Mutharaju, Raghava and
Sachdeva, Niharika and
Kumaraguru, Ponnurangam",
editor = "Vlachos, Andreas and
Augenstein, Isabelle",
booktitle = "Findings of the Association for Computational Linguistics: EACL 2023",
month = may,
year = "2023",
address = "Dubrovnik, Croatia",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2023.findings-eacl.163",
doi = "10.18653/v1/2023.findings-eacl.163",
pages = "2181--2191",
abstract = "Writing a good job description is an important step in the online recruitment process to hire the best candidates. Most recruiters forget to include some relevant skills in the job description. These missing skills affect the performance of recruitment tasks such as job suggestions, job search, candidate recommendations, etc. Existing approaches are limited to contextual modelling, do not exploit inter-relational structures like job-job and job-skill relationships, and are not scalable. In this paper, we exploit these structural relationships using a graph-based approach. We propose a novel skill prediction framework called JobXMLC, which uses graph neural networks with skill attention to predict missing skills using job descriptions. JobXMLC enables joint learning over a job-skill graph consisting of 22.8K entities (jobs and skills) and 650K relationships. We experiment with real-world recruitment datasets to evaluate our proposed approach. We train JobXMLC on 20,298 job descriptions and 2,548 skills within 30 minutes on a single GPU machine. JobXMLC outperforms the state-of-the-art approaches by 6{\%} in precision and 3{\%} in recall. JobXMLC is 18X faster for training task and up to 634X faster in skill prediction on benchmark datasets enabling JobXMLC to scale up on larger datasets.",
}
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<abstract>Writing a good job description is an important step in the online recruitment process to hire the best candidates. Most recruiters forget to include some relevant skills in the job description. These missing skills affect the performance of recruitment tasks such as job suggestions, job search, candidate recommendations, etc. Existing approaches are limited to contextual modelling, do not exploit inter-relational structures like job-job and job-skill relationships, and are not scalable. In this paper, we exploit these structural relationships using a graph-based approach. We propose a novel skill prediction framework called JobXMLC, which uses graph neural networks with skill attention to predict missing skills using job descriptions. JobXMLC enables joint learning over a job-skill graph consisting of 22.8K entities (jobs and skills) and 650K relationships. We experiment with real-world recruitment datasets to evaluate our proposed approach. We train JobXMLC on 20,298 job descriptions and 2,548 skills within 30 minutes on a single GPU machine. JobXMLC outperforms the state-of-the-art approaches by 6% in precision and 3% in recall. JobXMLC is 18X faster for training task and up to 634X faster in skill prediction on benchmark datasets enabling JobXMLC to scale up on larger datasets.</abstract>
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%0 Conference Proceedings
%T JobXMLC: EXtreme Multi-Label Classification of Job Skills with Graph Neural Networks
%A Goyal, Nidhi
%A Kalra, Jushaan
%A Sharma, Charu
%A Mutharaju, Raghava
%A Sachdeva, Niharika
%A Kumaraguru, Ponnurangam
%Y Vlachos, Andreas
%Y Augenstein, Isabelle
%S Findings of the Association for Computational Linguistics: EACL 2023
%D 2023
%8 May
%I Association for Computational Linguistics
%C Dubrovnik, Croatia
%F goyal-etal-2023-jobxmlc
%X Writing a good job description is an important step in the online recruitment process to hire the best candidates. Most recruiters forget to include some relevant skills in the job description. These missing skills affect the performance of recruitment tasks such as job suggestions, job search, candidate recommendations, etc. Existing approaches are limited to contextual modelling, do not exploit inter-relational structures like job-job and job-skill relationships, and are not scalable. In this paper, we exploit these structural relationships using a graph-based approach. We propose a novel skill prediction framework called JobXMLC, which uses graph neural networks with skill attention to predict missing skills using job descriptions. JobXMLC enables joint learning over a job-skill graph consisting of 22.8K entities (jobs and skills) and 650K relationships. We experiment with real-world recruitment datasets to evaluate our proposed approach. We train JobXMLC on 20,298 job descriptions and 2,548 skills within 30 minutes on a single GPU machine. JobXMLC outperforms the state-of-the-art approaches by 6% in precision and 3% in recall. JobXMLC is 18X faster for training task and up to 634X faster in skill prediction on benchmark datasets enabling JobXMLC to scale up on larger datasets.
%R 10.18653/v1/2023.findings-eacl.163
%U https://aclanthology.org/2023.findings-eacl.163
%U https://doi.org/10.18653/v1/2023.findings-eacl.163
%P 2181-2191
Markdown (Informal)
[JobXMLC: EXtreme Multi-Label Classification of Job Skills with Graph Neural Networks](https://aclanthology.org/2023.findings-eacl.163) (Goyal et al., Findings 2023)
ACL