Entity Extractor The Entity Extractor skill guides you through implementing named entity recognition (NER) systems that identify and classify entities in text. From people and organizations to domain-specific entities like products, medical terms, or financial instruments, this skill covers extraction approaches from simple pattern matching to advanced neural models. Entity extraction is a foundational NLP task that powers applications from search engines to knowledge graphs. Getting it right requires understanding your domain, choosing appropriate techniques, and handling the inherent ambiguity in natural language. Whether you need to extract standard entity types, define custom entities for your domain, or build relation extraction on top of entity recognition, this skill ensures your extraction pipeline is accurate and maintainable. Core Workflows Workflow 1: Choose Extraction Approach Define target entities: Standard types: PERSON, ORG, LOCATION, DATE, MONEY Domain-specific: PRODUCT, SYMPTOM, GENE, CONTRACT Relations: connections between entities Assess available resources: Labeled training data Domain expertise Compute constraints Select approach: Approach Training Data Accuracy Speed Customization spaCy (pre-trained) None Good Very fast Limited Rule-based None Variable Fast High Fine-tuned BERT 100s-1000s Excellent Medium Full LLM (zero-shot) None Good Slow Prompt-based LLM (few-shot) Few examples Very good Slow Prompt-based Plan implementation and evaluation Workflow 2: Implement Entity Extraction Pipeline Set up extraction: import spacy class EntityExtractor : def init ( self , model = "en_core_web_trf" ) : self . nlp = spacy . load ( model ) def extract ( self , text ) : doc = self . nlp ( text ) entities = [ ] for ent in doc . ents : entities . append ( { "text" : ent . text , "type" : ent . label_ , "start" : ent . start_char , "end" : ent . end_char , "confidence" : getattr ( ent , "confidence" , None ) } ) return entities def extract_batch ( self , texts ) : docs = list ( self . nlp . pipe ( texts ) ) return [ self . extract_from_doc ( doc ) for doc in docs ] Post-process entities: Normalize variations (IBM vs I.B.M.) Resolve abbreviations Link to knowledge base Validate extraction quality Handle edge cases Workflow 3: Build Custom Entity Recognizer Prepare training data:
Format for spaCy training
TRAIN_DATA
[ ( "Apple released the new iPhone today." , { "entities" : [ ( 0 , 5 , "ORG" ) , ( 24 , 30 , "PRODUCT" ) ] } ) , ( "Dr. Smith prescribed metformin for diabetes." , { "entities" : [ ( 0 , 9 , "PERSON" ) , ( 21 , 30 , "DRUG" ) , ( 35 , 43 , "CONDITION" ) ] } ) ] Configure training:
spaCy config for NER training
config
- {
- "training"
- :
- {
- "optimizer"
- :
- {
- "learn_rate"
- :
- 0.001
- }
- ,
- "batch_size"
- :
- {
- "@schedules"
- :
- "compounding"
- ,
- "start"
- :
- 4
- ,
- "stop"
- :
- 32
- }
- }
- ,
- "components"
- :
- {
- "ner"
- :
- {
- "factory"
- :
- "ner"
- ,
- "model"
- :
- {
- "@architectures"
- :
- "spacy.TransitionBasedParser"
- }
- }
- }
- }
- Train
- model:
- python
- -m
- spacy train config.cfg
- --output
- ./models
- --paths.train
- ./train.spacy
- --paths.dev
- ./dev.spacy
- Evaluate
- on held-out data
- Iterate
- based on errors
- Quick Reference
- Action
- Command/Trigger
- Extract entities
- "Extract entities from [text]"
- Choose NER model
- "Best NER for [domain]"
- Custom entities
- "Train custom entity recognizer"
- Evaluate NER
- "Evaluate entity extraction quality"
- Handle ambiguity
- "Resolve ambiguous entities"
- Entity linking
- "Link entities to knowledge base"
- Best Practices
- Start with Pre-trained
-
- Don't train from scratch unnecessarily
- spaCy, Hugging Face, and cloud APIs cover common entities
- Test pre-trained models first
- Fine-tune only when needed
- Define Clear Guidelines
-
- Entity boundaries are ambiguous
- "Dr. John Smith" - one entity or two?
- "New York Times" - ORG or GPE?
- Create and follow consistent annotation guidelines
- Handle Nested Entities
-
- Some entities contain others
- "Bank of America headquarters" (ORG inside LOCATION)
- Decide on nesting strategy upfront
- Some models support flat only; others handle nested
- Normalize Extracted Entities
-
- Raw text has variations
- "IBM", "I.B.M.", "International Business Machines"
- Canonicalize to standard form
- Link to knowledge base IDs when possible
- Evaluate Granularly
-
- Aggregate metrics hide issues
- Report precision/recall per entity type
- Analyze error patterns
- Test on edge cases explicitly
- Consider Context Window
- Models have context limits Long documents may need chunking Preserve context across chunks when possible Re-run on boundaries if entities might span Advanced Techniques LLM-Based Entity Extraction Use language models for flexible extraction: def llm_extract_entities ( text , entity_types ) : prompt = f"""Extract named entities from the following text. Text: " { text } " Entity types to extract: { chr ( 10 ) . join ( f"- { t } : { desc } " for t , desc in entity_types . items ( ) ) } Return a JSON array of entities: [{{"text": "entity text", "type": "ENTITY_TYPE", "start": 0, "end": 10}}] Only include entities that clearly match the specified types. """ response = llm . complete ( prompt , response_format = { "type" : "json_object" } ) return json . loads ( response ) [ "entities" ]
Example usage
entity_types
{ "COMPANY" : "Business organizations" , "PRODUCT" : "Commercial products or services" , "PERSON" : "Individual people's names" } entities = llm_extract_entities ( text , entity_types ) Hybrid Rule + ML Approach Combine patterns with neural extraction: class HybridExtractor : def init ( self ) : self . ml_extractor = spacy . load ( "en_core_web_trf" ) self . patterns = load_pattern_rules ( ) def extract ( self , text ) :
ML extraction
ml_entities
self . ml_extractor ( text ) . ents
Pattern-based extraction
pattern_entities
apply_patterns ( text , self . patterns )
Merge with priority rules
merged
merge_entities ( ml_entities , pattern_entities , priority = "pattern"
Patterns override ML when overlap
) return merged def add_pattern ( self , pattern , entity_type ) : """Add domain-specific pattern.""" self . patterns . append ( { "pattern" : pattern , "type" : entity_type } ) Entity Linking Connect extracted entities to knowledge bases: def link_entity ( entity_text , entity_type , knowledge_base ) : """ Link extracted entity to canonical entry in knowledge base. """
Generate candidates
candidates
knowledge_base . search ( query = entity_text , type_filter = entity_type , limit = 10 ) if not candidates : return { "entity" : entity_text , "linked" : None }
Score candidates
scored
[ ] for candidate in candidates : score = compute_linking_score ( entity_text , candidate . name , candidate . aliases ) scored . append ( ( candidate , score ) )
Select best match
best
max ( scored , key = lambda x : x [ 1 ] ) if best [ 1 ]
LINKING_THRESHOLD : return { "entity" : entity_text , "linked" : best [ 0 ] . id , "canonical_name" : best [ 0 ] . name , "confidence" : best [ 1 ] } else : return { "entity" : entity_text , "linked" : None } Relation Extraction Extract relationships between entities: def extract_relations ( text , entities ) : """ Given extracted entities, find relations between them. """ prompt = f"""Given this text and extracted entities, identify relationships. Text: " { text } " Entities found: { json . dumps ( entities , indent = 2 ) } Identify relationships between entities. Return JSON: [{{ "subject": "entity text", "relation": "relationship type", "object": "entity text", "confidence": 0.9 }}] Common relation types: WORKS_FOR, LOCATED_IN, FOUNDED, ACQUIRED, PARTNER_OF """ response = llm . complete ( prompt ) return json . loads ( response ) Active Learning for NER Efficiently improve extraction with targeted labeling: def active_learning_sample ( unlabeled_texts , model , n_samples = 100 ) : """ Select texts that would be most valuable to label. """ uncertainties = [ ] for text in unlabeled_texts : doc = model ( text )
Calculate uncertainty (various strategies)
uncertainty
calculate_ner_uncertainty ( doc ) uncertainties . append ( ( text , uncertainty ) )
Select most uncertain
uncertainties . sort ( key = lambda x : x [ 1 ] , reverse = True ) return [ text for text , _ in uncertainties [ : n_samples ] ] def calculate_ner_uncertainty ( doc ) : """ Calculate uncertainty based on entity confidence scores. """ if not doc . ents : return 0.5
No entities - medium uncertainty
confidences
[ ent . _ . confidence for ent in doc . ents if hasattr ( ent . _ , "confidence" ) ] if not confidences : return 0.5
High uncertainty = low confidence entities
return 1 - min ( confidences ) Common Pitfalls to Avoid Inconsistent annotation guidelines leading to noisy training data Not handling entity boundary ambiguity (where does entity end?) Ignoring nested or overlapping entities when they matter Training on small datasets without augmentation Not normalizing entities before downstream use Assuming pre-trained models work on your domain without testing Not evaluating per-entity-type performance Forgetting about entity linking for disambiguation