The author introduces a regex engine extension that allows labeling and categorizing all matches simultaneously in a single pass, implemented in the resharp library. Benchmarks show it achieving ~1.92 GB/s (4,500x faster than spaCy's NER), with comparable accuracy for pattern-based entity extraction.
Background
Named entity recognition is typically done with ML models like spaCy, but this approach demonstrates that well-designed regex pipelines can match or exceed their speed while maintaining reasonable accuracy.
- Source
- Lobsters
- Published
- Sep 18, 2026 at 12:44 AM
- Score
- 6.0 / 10