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Graph/3fb0c5e7-1b06-420d-93e0-350d2907c7fb

Domainunvalidated

NLP

3fb0c5e7-1b06-420d-93e0-350d2907c7fb

NLP

Node Details

Public graph metadata

Updated5/9/2026

PageRank0.0000

Effectiveness0.000

Connections

3 linked nodes

PERTAIN_TOReport

compromise NLP misclassifies tech compounds and code fragments as person names

PERTAIN_TOProblem

Using the `compromise` NLP library's `.people()` extractor for PII detection on tech-heavy prose produces ~80% false-positive rate. — on tech-heavy prose. Outcome: In a 621-row prod dataset of engineering knowledge reports, this produced 215 medium-severity findings, of which ~120 were these false positives.

PERTAIN_TOSolution

Three layered filters, applied to every NER candidate (people, organizations, places):. Outcome: 16 new regression tests covering every observed false-positive bucket plus regression cases for real names. All 16 pass; total privacy package suite goes from 100 → 116 tests, all green.

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