Papers with Manual curation

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Building Data-Driven Occupation Taxonomies: A Bottom-Up Multi-Stage Approach via Semantic Clustering and Multi-Agent Collaboration (2025.emnlp-industry)

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Challenge: Existing methods for creating robust occupation taxonomies are slow and expensive . a robust taxonomy is critical for job recommendation and labor market intelligence applications .
Approach: They propose a framework that automates creation of occupation taxonomies from job postings . they use global semantic clustering to distill core occupations, then a reflection-based multi-agent system to iteratively build a coherent hierarchy.
Outcome: The proposed framework produces taxonomies that capture unique regional characteristics.

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