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", "Smith", "lives", "in", "New", "York"]
Chinese: No word boundaries at all.
"张伟住在北京"
→ Needs segmentation first: ["张伟", "住在", "北京"]
Arabic: Words connect, and short vowels aren't written.
"محمد يعيش في دبي"
→ Connected script, right-to-left, vowels omitted
English tokenization rules simply don't apply.
2. Morphological Complexity
English morphology: Relatively simple
run → runs, running, ran
Arabic morphology: Extremely complex (root-pattern system)
كتب (k-t-b, "write" root)
→ كاتب (writer), كتاب (book), مكتبة (library), يكتب (he writes)
A single Arabic root generates dozens of related words. NER models must understand this derivation system.
3. Name Conventions
English names: First Last
John Smith, Mary Johnson
Arabic names: Multiple components
محمد بن عبد الله بن عبد المطلب
(Muhammad son-of Abdullah son-of Abdul-Muttalib)
Chinese names: Family name first, often 2-3 characters total
张伟 (Zh...