Introduction
Entity extraction, or Named Entity Recognition (NER), is pivotal for transforming unstructured text into structured data. It underpins applications like resume parsing, contract analysis, and medical record processing. DSPy provides the tools to build sophisticated extraction systems that handle real-world complexity.
Understanding Entity Extraction
Common Entity Types
- Person: John Smith, Dr. Sarah Johnson
- Organization: Google, Microsoft, Stanford University
- Location: New York, 123 Main Street
- Date/Time: January 15, 2024, 3:30 PM
- Money: $50,000, €1.2 million
Real-World Applications
- Resume Processing: Extract skills, experience, and education.
- Contract Analysis: Identify parties, dates, and clauses.
- Medical Records: Extract diagnoses, medications, and procedures.
Building Entity Extractors
Basic Entity Extractor
A simple module to extract specified entity types:
Python
import dspy
class BasicEntityExtractor(dspy.Module):
def __init__(self, entity_types):
super().__init__()
self.entity_types = entity_types
types_str = ", ".join(entity_types)
self.extract = dspy.Predict(
f"text, entity_types[{types_str}] -> entities"
)
def forward(self, text):
result = self.extract(
text=text,
entity_types=", ".join(self.entity_types)
)
# Assume result.entities is parsed into a structured list
return dspy.Prediction(
entities=result.entities,
raw_output=result.entities
)Advanced Entity Extractor with Context
Handling context, validation, and disambiguation:
Python
class AdvancedEntityExtractor(dspy.Module):
def __init__(self, entity_types, context_window=100):
super().__init__()
self.entity_types = entity_types
types_str = ", ".join(entity_types)
self.find_entities = dspy.ChainOfThought(
f"text, context, entity_types[{types_str}] -> entities_with_positions"
)
self.validate_entities = dspy.Predict(
"entity, text_context -> is_valid, corrected_entity, confidence"
)
self.disambiguate = dspy.Predict(
"entity, context, possible_meanings -> disambiguated_entity, reasoning"
)
def forward(self, text, document_context=None):
context = document_context[-100:] if document_context else text
# Find entities
extraction = self.find_entities(
text=text,
context=context,
entity_types=", ".join(self.entity_types)
)
# Logic to parse, validate, and disambiguate entities would follow...
# Returns structured entities list
return dspy.Prediction(
entities=extraction.entities_with_positions,
extraction_reasoning=extraction.rationale
)Specialized Applications
Resume/CV Parser
Extracting structured data from resumes:
Python
class ResumeParser(dspy.Module):
def __init__(self):
super().__init__()
self.contact_info = dspy.Predict(
"resume_text -> name, email, phone, location, linkedin"
)
self.extract_sections = dspy.Predict(
"resume_text -> work_experience, education, skills, certifications"
)
self.parse_experience = dspy.ChainOfThought(
"experience_section -> detailed_experiences"
)
def forward(self, resume_text):
contact = self.contact_info(resume_text=resume_text)
sections = self.extract_sections(resume_text=resume_text)
# Detailed parsing logic...
return dspy.Prediction(
contact_info=contact,
sections=sections
)Best Practices
- Ambiguity: Use context to disambiguate entities (e.g., "Apple" fruit vs. company).
- Validation: Use regex or rules to validate specific types like emails or dates.
- Nested Entities: Handle overlapping entities carefully (e.g., "University of [California]").
- Optimization: Use
BootstrapFewShotwith a custom F1 metric to improve extraction performance.