Language Studion Custom Named Entity Recognition Approach

Question

What approach is utilized in Azure Language Studio for Custom Named Entity Recognition (NER)?

What type of neural networks or models are trained in this process? Are they Transformer-based models like BERT, RoBERTa, or another architecture?

Is there a way to find detailed information about the model used and the training methods directly in the Azure documentation or the Azure portal?

Answer 1

Hi @Marlene,

Thank you for reaching out to Microsoft Q&A forum!

Azure Language Studio for Custom Named Entity Recognition (NER) utilizes a machine learning approach to build custom models for domain-specific entity extraction tasks.

Regarding the type of neural networks or models trained in this process, the Azure documentation does not provide specific information about the architecture used for Custom NER. However, the documentation mentions that the service applies machine-learning intelligence to enable users to build custom models for custom named entity recognition tasks.

Regarding the model used and the training methods, the documentation provides information on how to train your custom named entity recognition model, which includes information on how to prepare your data, split your data, and start a training job. The documentation also provides information on how to view your model's performance to determine if you need to improve your model. However, the documentation does not provide detailed information about the model architecture used or the training methods.

See: NER overview and custom NER overview.

I hope you understand. And, if you have any further query do let us know.

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