Deep Learning-based Computational Job Market Analysis: A Survey on Skill Extraction and Classification from Job Postings

TL;DR

This survey reviews deep learning methods, datasets, and terminology used in NLP-based skill extraction and classification from job postings, highlighting current challenges and resources in this rapidly evolving field.

Contribution

It provides the first comprehensive overview of deep learning approaches, datasets, and terminology specific to NLP-driven skill extraction and classification in the job market.

Findings

Catalogs publicly available datasets for skill extraction

Highlights lack of standardized terminology in the field

Identifies key deep learning models used in skill classification

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 emerging 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 and classification. 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.