# Entity Extraction and NLP Tuning

# Entity Extraction and NLP Tuning

Tuning your Natural Language Processing (NLP) pipelines is essential for high-quality entity extraction and search relevance.

# Understanding the NLP Pipeline

The NLP pipeline processes text to extract:

• Entities: People, Organizations, Locations, Dates, etc.
• Signals: Custom identifiers, keywords, or concepts.
• Embeddings: Vector representations for semantic search.

# Tuning Extraction Quality

• Custom Spotters: Define custom patterns (regex or dictionary-based) to extract industry-specific terms (e.g., product IDs, legal codes).
• Language Models: Select the most appropriate model for your data's language and domain.
• Confidence Thresholds: Adjust thresholds to balance precision and recall for extracted entities.

# Evaluating NLP Performance

• Precision: The percentage of extracted entities that are correct.
• Recall: The percentage of actual entities in the text that were successfully extracted.
• F1 Score: The harmonic mean of precision and recall.

# Iterative Improvement

1. Annotate: Create a small "gold standard" dataset of manually labeled examples.
2. Test: Run your NLP pipeline against the annotated data.
3. Analyze: Identify common errors (false positives/negatives).
4. Adjust: Update your spotters, models, or configurations.
5. Repeat: Continuously monitor and refine the pipeline as your data evolves.

# Next Steps

• Learn about Embeddings
• Learn about Entity Extraction