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20192026
most citedSkillSpan: Hard and Soft Skill Extraction from English Job Postings

7 citations · 27 across the 14 of their papers we have counts for

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18 papers · 1 filter

cs.CL2026

Effective Performance Measurement: Challenges and Opportunities in KPI Extraction from Earnings Calls

Rasmus T. Aavang, Rasmus Tjalk-Bøggild, Alexandre Iolov +3

Earnings calls are a key source of financial information about public companies. However, extracting information from these calls is difficult. Unlike the templatic filings require…

cs.CL2026

WorkRB: A Community-Driven Evaluation Framework for AI in the Work Domain

Matthias De Lange, Warre Veys, Federico Retyk +16

Today's evolving labor markets rely increasingly on recommender systems for hiring, talent management, and workforce analytics, with natural language processing (NLP) capabilities…

cs.CL2026

UniSkill: A Dataset for Matching University Curricula to Professional Competencies

Nurlan Musazade, Joszef Mezei, Mike Zhang

Skill extraction and recommendation systems have been studied from recruiter, applicant, and education perspectives. While AI applications in job advertisements have received broad…

cs.CL2026

Do Large Language Models Adapt to Language Variation across Socioeconomic Status?

Elisa Bassignana, Mike Zhang, Dirk Hovy +1

Humans adjust their linguistic style to the audience they are addressing. However, the extent to which LLMs adapt to different social contexts is largely unknown. As these models i…

cs.CL2025

NLPnorth @ TalentCLEF 2025: Comparing Discriminative, Contrastive, and Prompt-Based Methods for Job Title and Skill Matching

Mike Zhang, Rob van der Goot

Matching job titles is a highly relevant task in the computational job market domain, as it improves e.g., automatic candidate matching, career path prediction, and job market anal…

cs.CL2025

Follow the Path: Reasoning over Knowledge Graph Paths to Improve Large Language Model Factuality

Mike Zhang, Johannes Bjerva, Russa Biswas

We introduce fs1, a simple yet effective method that improves the factuality of reasoning traces by collecting them from large reasoning models and grounding them in knowledge grap…