Skills demonstration
Instruction and application
In ProgressUsing SpaCy for NER demo
In this demo, we’ll customise SpaCy’s NER models to recognise industry-specific entities, enhancing their relevance for government and various sectors.
This hands-on session demonstrates how to adapt NER for specialised use cases, making it more effective for business applications like regulatory compliance, customer insights and automated document processing.
Demo steps
Custom entity configuration
Set up the EntityRuler pipeline component and define patterns for domain-specific entities like government organisations, auto makers, financial institutions, tech companies and media outlets.
Apply custom NER to tweets
Process tweets using the enhanced NER pipeline to extract mentions of custom entities, focusing on specific sectors and industries.
Analyse entity distributions
Create visualisations to understand the frequency and distribution of custom entity types within the dataset.
Key takeaways
Custom entity recognition
EntityRuler extends SpaCy’s NER by combining pattern-based matching with statistical models for specialised tasks.
Pattern definition
Label-pattern pairs define entity types.
- Case-insensitive matching improves recall.
- Multiple patterns can map to the same entity.
Pipeline integration
EntityRuler runs before statistical NER, preserving original capabilities while adding domain-specific entities.
Entity analysis
Frequency analysis, distribution insights and visualisation help track entity patterns across industries.