Papers
Topics
Authors
Recent
Gemini 2.5 Flash
Gemini 2.5 Flash
119 tokens/sec
GPT-4o
56 tokens/sec
Gemini 2.5 Pro Pro
43 tokens/sec
o3 Pro
6 tokens/sec
GPT-4.1 Pro
47 tokens/sec
DeepSeek R1 via Azure Pro
28 tokens/sec
2000 character limit reached

Deep Learning-based Computational Job Market Analysis: A Survey on Skill Extraction and Classification from Job Postings (2402.05617v1)

Published 8 Feb 2024 in cs.CL

Abstract: Recent years have brought significant advances to 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.

User Edit Pencil Streamline Icon: https://streamlinehq.com
Authors (4)
  1. Elena Senger (2 papers)
  2. Mike Zhang (33 papers)
  3. Rob van der Goot (38 papers)
  4. Barbara Plank (130 papers)
Citations (3)

Summary

We haven't generated a summary for this paper yet.