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Named Entity Recognition (NER) is the information extraction task of identifying and classifying mentions of locations, quantities, monetary values, organizations, people, and other named entities within a text.

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Named-Entity-Recognition

Named Entity Recognition (NER) is the information extraction task of identifying and classifying mentions of locations, quantities, monetary values, organizations, people, and other named entities within a text.

Objective

To identify sensitive NER for document redaction which includes Social Security Numbers (Aadhaar Number), Bank account numbers and related sensitive information, Name, Place, Organization. (Vary from organization to organization for data anonymity objective)

1. Named Entity Recognition

Model training approachs

  • Transfer learning on Spacy
  • Using RNN and BiLSTM
  • Using Pytorch and BERT

References

[1] Neural Architectures for Named Entity Recognition - published on arxiv.org

[2] NER using Pytorch and BERT

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Named Entity Recognition (NER) is the information extraction task of identifying and classifying mentions of locations, quantities, monetary values, organizations, people, and other named entities within a text.

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