Deep Learning-based Computational Job Market Analysis: A Survey on Skill Extraction and Classification from Job Postings
- Elena Senger,
- Mike Zhang,
- ,
- Ludwig Maximilian University of Munich,
- ,
Research Output:
Conference Article in Proceeding or Book/Report chapter
Article in proceedings
Research
Peer-reviewOpen access
Abstract
Recent years have brought significant advances to Natural Language Processing (NLP), which enabled fast progress in the field of computational job market analysis. Core tasks in this application domain are skill extraction and classification from job postings. Because of its quick growth and its interdisciplinary nature, there is no exhaustive assessment of this field. This survey aims to fill this gap by providing a comprehensive overview of deep learning methodologies, datasets, and terminologies specific to NLP-driven skill extraction. Our comprehensive cataloging of publicly available datasets addresses the lack of consolidated information on dataset creation and characteristics. Finally, the focus on terminology addresses the current lack of consistent definitions for important concepts, such as hard and soft skills, and terms relating to skill extraction and classification.
Publication Information
Output type
Research Output:
Conference Article in Proceeding or Book/Report chapter
Article in proceedings
Research
Peer-reviewOriginal language
EnglishPages from-to (Number of pages)
Pages 1–15 (15 pages)Publication milestones
- Published - 03/2024
Publication status
Published - 03/2024
Publisher
Association for Computational Linguistics, United StatesHost publication title
1st Workshop on Natural Language Processing for Human ResourcesAccess to documents
Final published version
Final published version
License:CC BY, opens in new tab
Related Event
Title
Natural Language Processing for Human Resources workshop
Event type
WorkshopLinks
Degree of recognition
International eventDate
22/03/2024 - 22/03/2024Location
St. JuliansMalta
