AWED-PIPER: Agents, Web Applications & Expert Detectors for Personally Identifiable Information Protection & Fine-grained Named Entity Recognition across 36 languages for 6.6 Billion Speakers

Title:AWED-PIPER: Agents, Web Applications & Expert Detectors for Personally Identifiable Information Protection & Fine-grained Named Entity Recognition across 36 languages for 6.6 Billion Speakers

Abstract:Named Entity Recognition (NER) and Personally Identifiable Information (PII) anonymization are critical tasks in Natural Language Processing (NLP) for information extraction and privacy preservation. We introduce AWED-PIPER, an open-source framework comprising agentic tools, interactive web applications, and 54 state-of-the-art expert detector models that provide unified Fine-grained Named Entity Recognition (FgNER) and reversible synthetic PII pseudonymization across 36 languages spoken by over 6.6 billion people. The system couples fine-grained multilingual sequence labeling with script-aware regex detectors to identify contextual entities (Person, Location, Organization, Medical) as well as structured technical PII (Emails, native-script Phone Numbers, IP Addresses, Credit Cards). AWED-PIPER offers a dual capability: full FgNER entity extraction and privacy-preserving reversible anonymization with persistent placeholders and de-anonymization dictionary mappings. The suite spans global languages to extremely low-resource vulnerable languages like Bodo, Manipuri, Bishnupriya, and Mizo. The resources can be accessed here: PII Protector Agentic Tool: (this https URL), FgNER Agentic Tool: (this https URL), PII Web Application: (this https URL), FgNER Web Application: (this https URL), and Edge-deployable Expert Detector Models: (this https URL).

Computation and Language (cs.CL); Artificial Intelligence (cs.AI); Information Retrieval (cs.IR)

arXiv:2601.10161 [cs.CL]

(or arXiv:2601.10161v3 [cs.CL] for this version)

https://doi.org/10.48550/arXiv.2601.10161 Focus to learn more arXiv-issued DOI via DataCite

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