Filtering job posting data before LLM-assisted clustering and hierarchical labeling yields taxonomies with better AI skill coverage than unfiltered approaches.
Proceedings of the 2025 Annual International
2 Pith papers cite this work. Polarity classification is still indexing.
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cs.CL 2years
2026 2verdicts
UNVERDICTED 2representative citing papers
TingIS uses multi-stage LLM event linking plus routing and filtering to extract high-priority incidents from noisy customer data at 2,000 messages per minute, delivering 3.5-minute P90 latency and 95% discovery in production.
citing papers explorer
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Building a Custom Taxonomy of AI Skills and Tasks from the Ground Up with Job Postings
Filtering job posting data before LLM-assisted clustering and hierarchical labeling yields taxonomies with better AI skill coverage than unfiltered approaches.
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TingIS: Real-time Risk Event Discovery from Noisy Customer Incidents at Enterprise Scale
TingIS uses multi-stage LLM event linking plus routing and filtering to extract high-priority incidents from noisy customer data at 2,000 messages per minute, delivering 3.5-minute P90 latency and 95% discovery in production.