Design of Negative Sampling Strategies for Distantly Supervised Skill Extraction

13 September 2022

Papers citing "Design of Negative Sampling Strategies for Distantly Supervised Skill Extraction"

9 / 9 papers shown

Title
A Multi-Stage Framework with Taxonomy-Guided Reasoning for Occupation Classification Using Large Language Models Palakorn Achananuparp Ee-Peng Lim 46 0 0 17 Mar 2025
MELO: An Evaluation Benchmark for Multilingual Entity Linking of Occupations Federico Retyk Luis Gasco Casimiro Pio Carrino Daniel Deniz Rabih Zbib 33 0 0 10 Oct 2024
Deep Learning-based Computational Job Market Analysis: A Survey on Skill Extraction and Classification from Job Postings Elena Senger Mike Zhang Rob van der Goot Barbara Plank 34 7 0 08 Feb 2024
JOBSKAPE: A Framework for Generating Synthetic Job Postings to Enhance Skill Matching Antoine Magron Anna Dai Mike Zhang Syrielle Montariol Antoine Bosselut SyDa 41 7 0 05 Feb 2024
NNOSE: Nearest Neighbor Occupational Skill Extraction Mike Zhang Rob van der Goot Min-Yen Kan Barbara Plank 37 3 0 30 Jan 2024
In-Context Learning for Extreme Multi-Label Classification Karel DÓosterlinck Omar Khattab François Remy Thomas Demeester Chris Develder Christopher Potts 31 2 0 22 Jan 2024
Extreme Multi-Label Skill Extraction Training using Large Language Models Jens-Joris Decorte S. Verlinden Jeroen Van Hautte Johannes Deleu Chris Develder Thomas Demeester 36 14 0 20 Jul 2023
Large Language Models as Batteries-Included Zero-Shot ESCO Skills Matchers Benjamin Clavié Guillaume Soulié 26 11 0 07 Jul 2023
ESCOXLM-R: Multilingual Taxonomy-driven Pre-training for the Job Market Domain Mike Zhang Rob van der Goot Barbara Plank 24 14 0 20 May 2023