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How to improve entity recognition accuracy in Rasa?

Asked on Nov 03, 2025

Answer

Improving entity recognition accuracy in Rasa involves refining your training data and model configuration to better capture the entities relevant to your use case. This process often includes enhancing your NLU training data and adjusting model parameters for optimal performance.

Example Concept: To enhance entity recognition in Rasa, ensure your NLU training data includes diverse and representative examples of the entities you want to recognize. Use Rasa's lookup tables and regex features to define patterns for specific entities, and consider using custom components or pre-trained embeddings to boost model understanding of complex entities.

Additional Comment:

Regularly review and update your training data to include new examples and variations of entity expressions.
Utilize Rasa's interactive learning feature to refine entity recognition through real-time feedback.
Consider using Rasa's DIET classifier, which supports multi-task learning and can improve entity recognition accuracy.
Experiment with different configurations and pipelines to find the best setup for your specific needs.

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How to improve entity recognition accuracy in Rasa?

Asked on Nov 03, 2025

Answer

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