Standard
Joint Extraction and Classification of Danish Competences for Job Matching. / Li, Qiuchi; Lioma, Christina.
Advances in Information Retrieval : 45th European Conference on Information Retrieval, ECIR 2023, Dublin, Ireland, April 2–6, 2023, Proceedings, Part II. red. / Jaap Kamps; Lorraine Goeuriot; Fabio Crestani; Maria Maistro; Hideo Joho; Brian Davis; Cathal Gurrin; Udo Kruschwitz; Annalina Caputo. Springer, 2023. s. 475-483 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).
Publikation: Bidrag til bog/antologi/rapport › Konferencebidrag i proceedings › Forskning › fagfællebedømt
Harvard
Li, Q & Lioma, C 2023,
Joint Extraction and Classification of Danish Competences for Job Matching. i J Kamps, L Goeuriot, F Crestani, M Maistro, H Joho, B Davis, C Gurrin, U Kruschwitz & A Caputo (red),
Advances in Information Retrieval : 45th European Conference on Information Retrieval, ECIR 2023, Dublin, Ireland, April 2–6, 2023, Proceedings, Part II. Springer, Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), s. 475-483, 45th European Conference on Information Retrieval, ECIR 2023, Dublin, Irland,
02/04/2023.
https://doi.org/10.1007/978-3-031-28238-6_38
APA
Li, Q., & Lioma, C. (2023).
Joint Extraction and Classification of Danish Competences for Job Matching. I J. Kamps, L. Goeuriot, F. Crestani, M. Maistro, H. Joho, B. Davis, C. Gurrin, U. Kruschwitz, & A. Caputo (red.),
Advances in Information Retrieval : 45th European Conference on Information Retrieval, ECIR 2023, Dublin, Ireland, April 2–6, 2023, Proceedings, Part II (s. 475-483). Springer. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
https://doi.org/10.1007/978-3-031-28238-6_38
Vancouver
Li Q, Lioma C.
Joint Extraction and Classification of Danish Competences for Job Matching. I Kamps J, Goeuriot L, Crestani F, Maistro M, Joho H, Davis B, Gurrin C, Kruschwitz U, Caputo A, red., Advances in Information Retrieval : 45th European Conference on Information Retrieval, ECIR 2023, Dublin, Ireland, April 2–6, 2023, Proceedings, Part II. Springer. 2023. s. 475-483. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).
https://doi.org/10.1007/978-3-031-28238-6_38
Author
Li, Qiuchi ; Lioma, Christina. / Joint Extraction and Classification of Danish Competences for Job Matching. Advances in Information Retrieval : 45th European Conference on Information Retrieval, ECIR 2023, Dublin, Ireland, April 2–6, 2023, Proceedings, Part II. red. / Jaap Kamps ; Lorraine Goeuriot ; Fabio Crestani ; Maria Maistro ; Hideo Joho ; Brian Davis ; Cathal Gurrin ; Udo Kruschwitz ; Annalina Caputo. Springer, 2023. s. 475-483 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).
Bibtex
@inproceedings{2f6e43145611452199572b9b383e4ac6,
title = "Joint Extraction and Classification of Danish Competences for Job Matching",
abstract = "The matching of competences, such as skills, occupations or knowledges, is a key desiderata for candidates to be fit for jobs. Automatic extraction of competences from CVs and Jobs can greatly promote recruiters{\textquoteright} productivity in locating relevant candidates for job vacancies. This work presents the first model that jointly extracts and classifies competence from Danish job postings. Different from existing works on skill extraction and skill classification, our model is trained on a large volume of annotated Danish corpora and is capable of extracting a wide range of danish competences, including skills, occupations and knowledges of different categories. More importantly, as a single BERT-like architecture for joint extraction and classification, our model is lightweight and efficient at inference. On a real-scenario job matching dataset, our model beats the state-of-the-art models in the overall performance of Danish competence extraction and classification, and saves over 50% time at inference.",
keywords = "Competence extraction and classification, Danish BERT, Job matching",
author = "Qiuchi Li and Christina Lioma",
note = "Funding Information: Acknowledgement. This research was supported by the Innovation Fund Denmark, grant no. 0175-000005B. We are grateful for Jobindex{\textquoteright}s support on providing the data and setting up the experiment. Publisher Copyright: {\textcopyright} 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.; 45th European Conference on Information Retrieval, ECIR 2023 ; Conference date: 02-04-2023 Through 06-04-2023",
year = "2023",
doi = "10.1007/978-3-031-28238-6_38",
language = "English",
isbn = "978-3-031-28237-9",
series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",
publisher = "Springer",
pages = "475--483",
editor = "Jaap Kamps and Lorraine Goeuriot and Fabio Crestani and Maria Maistro and Hideo Joho and Brian Davis and Cathal Gurrin and Udo Kruschwitz and Annalina Caputo",
booktitle = "Advances in Information Retrieval",
address = "Switzerland",
}
RIS
TY - GEN
T1 - Joint Extraction and Classification of Danish Competences for Job Matching
AU - Li, Qiuchi
AU - Lioma, Christina
N1 - Funding Information:
Acknowledgement. This research was supported by the Innovation Fund Denmark, grant no. 0175-000005B. We are grateful for Jobindex’s support on providing the data and setting up the experiment.
Publisher Copyright:
© 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.
PY - 2023
Y1 - 2023
N2 - The matching of competences, such as skills, occupations or knowledges, is a key desiderata for candidates to be fit for jobs. Automatic extraction of competences from CVs and Jobs can greatly promote recruiters’ productivity in locating relevant candidates for job vacancies. This work presents the first model that jointly extracts and classifies competence from Danish job postings. Different from existing works on skill extraction and skill classification, our model is trained on a large volume of annotated Danish corpora and is capable of extracting a wide range of danish competences, including skills, occupations and knowledges of different categories. More importantly, as a single BERT-like architecture for joint extraction and classification, our model is lightweight and efficient at inference. On a real-scenario job matching dataset, our model beats the state-of-the-art models in the overall performance of Danish competence extraction and classification, and saves over 50% time at inference.
AB - The matching of competences, such as skills, occupations or knowledges, is a key desiderata for candidates to be fit for jobs. Automatic extraction of competences from CVs and Jobs can greatly promote recruiters’ productivity in locating relevant candidates for job vacancies. This work presents the first model that jointly extracts and classifies competence from Danish job postings. Different from existing works on skill extraction and skill classification, our model is trained on a large volume of annotated Danish corpora and is capable of extracting a wide range of danish competences, including skills, occupations and knowledges of different categories. More importantly, as a single BERT-like architecture for joint extraction and classification, our model is lightweight and efficient at inference. On a real-scenario job matching dataset, our model beats the state-of-the-art models in the overall performance of Danish competence extraction and classification, and saves over 50% time at inference.
KW - Competence extraction and classification
KW - Danish BERT
KW - Job matching
U2 - 10.1007/978-3-031-28238-6_38
DO - 10.1007/978-3-031-28238-6_38
M3 - Article in proceedings
AN - SCOPUS:85150976806
SN - 978-3-031-28237-9
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 475
EP - 483
BT - Advances in Information Retrieval
A2 - Kamps, Jaap
A2 - Goeuriot, Lorraine
A2 - Crestani, Fabio
A2 - Maistro, Maria
A2 - Joho, Hideo
A2 - Davis, Brian
A2 - Gurrin, Cathal
A2 - Kruschwitz, Udo
A2 - Caputo, Annalina
PB - Springer
T2 - 45th European Conference on Information Retrieval, ECIR 2023
Y2 - 2 April 2023 through 6 April 2023
ER -
