Cross-Domain Transfer and Few-Shot Learning for Personal Identifiable Information Recognition
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Abstract (arXiv)
Accurate recognition of personally identifiable information (PII) is central to automated text anonymization. This paper investigates the effectiveness of cross-domain model transfer, multi-domain data fusion, and sample-efficient learning for PII recognition. Using annotated corpora from healthcare (I2B2), legal (TAB), and biography (Wikipedia), we evaluate models across four dimensions: in-domain performance, cross-domain transferability, fusion, and few-shot learning. Results show legal-domain data transfers well to biographical texts, while medical domains resist incoming transfer. Fusion benefits are domain-specific, and high-quality recognition is achievable with only 10% of training data in low-specialization domains.
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References · 5
- 2022The Text Anonymization Benchmark (TAB): A Dedicated Corpus and Evaluation Framework for Text Anonymizationcited by 95
- 2016De-identification of Patient Notes with Recurrent Neural Networkscited by 88
- 2014C-sanitized: a privacy model for document redaction and sanitizationcited by 27
- 2021De-identification of Privacy-related Entities in Job Postingscited by 12
- 2022Bootstrapping Text Anonymization Models with Distant Supervisioncited by 8
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