[LT-01] Multi-Language NER: Why Your English-Trained...

[LT-01]

The Multilingual NER Challenge

Named Entity Recognition (NER) models trained on English achieve impressive results—85-92% F1 scores on standard benchmarks. Apply those same models to Arabic or Chinese? Accuracy often drops to 50-70%.

For PII detection, this gap is critical. A 70% detection rate means 30% of sensitive data goes unprotected.

Why English Models Fail

1. Word Boundaries

English: Words are separated by spaces.

"John Smith lives in New York"
→ ["John", "Smit...