How do I integrate named entity recognition NER into a Generative AI model for producing structured data

Question

Can you tell me How do I integrate named entity recognition (NER) into a Generative AI model for producing structured data?

Answer 1

To integrate Named Entity Recognition (NER) into a Generative AI model for structured data production, use a fine-tuned transformer-based NER model (e.g., spaCy, Hugging Face Transformers), extract entities, and format them into structured output using templates or JSON serialization.

Here is the code snippet you can refer to:

In the above, we are using the following key points:

• Pre-trained NER Model: Uses spaCy's en_core_web_sm model for entity recognition.
• Named Entity Extraction: Identifies and categorizes entities such as persons, organizations, and locations.
• Structured JSON Output: Converts extracted entities into a structured format for easy integration.
• Dynamic Entity Grouping: Organizes entities by type to facilitate structured data generation.
• Scalability: This can be integrated into a Generative AI pipeline to enrich text generation with structured knowledge.

Hence, by combining NER with structured JSON formatting, we can integrate named entity recognition into Generative AI models to enhance structured data extraction and organization efficiently.