mlsanityData to 14 Oct 2026

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

arXiv 2601.10161 ↗First posted 15 Jan 2026cs.CL cs.AI cs.IRcited by 0

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Abstract (arXiv)

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: (https://github.com/PrachuryyaKaushik/AWED-PIPER), FgNER Agentic Tool: (https://github.com/PrachuryyaKaushik/AWED-FiNER), PII Web Application: (https://hf.co/spaces/prachuryyaIITG/AWED_PII_Protector), FgNER Web Application: (https://hf.co/spaces/prachuryyaIITG/AWED-FiNER), and Edge-deployable Expert Detector Models: (https://hf.co/collections/prachuryyaIITG/awed-piper).

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References · 12

  1. 2019Unsupervised Cross-lingual Representation Learning at Scalecited by 3,935
  2. 2020Stanza: A Python Natural Language Processing Toolkit for Many Human Languagescited by 723
  3. 2021MuRIL: Multilingual Representations for Indian Languagescited by 179
  4. 2019Gradio: Hassle-Free Sharing and Testing of ML Models in the Wildcited by 178
  5. 2021Few-NERD: A Few-Shot Named Entity Recognition Datasetcited by 152
  6. 2022Towards Leaving No Indic Language Behind: Building Monolingual Corpora, Benchmark and Models for Indic Languagescited by 102
  7. 2022Naamapadam: A Large-Scale Named Entity Annotated Data for Indic Languagescited by 28
  8. 2022L3Cube-MahaNER: A Marathi Named Entity Recognition Dataset and BERT modelscited by 21
  9. 2022HiNER: A Large Hindi Named Entity Recognition Datasetcited by 12
  10. 2019A Finnish News Corpus for Named Entity Recognitioncited by 11
  11. 2021You Get What You Chat: Using Conversations to Personalize Search-based Recommendationscited by 4
  12. 2025NameTag 3: A Tool and a Service for Multilingual/Multitagset NERcited by 1

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