Information Extraction › Summary

Information extraction turns free-form text into structured data. We covered the four main tasks in the IE stack:

• Named entity recognition identifies and classifies entity mentions in text using BIO sequence labeling. Modern transformer-based NER models like spaCy's en_core_web_trf achieve high accuracy with minimal setup. The key insight is that contextual representations from bidirectional transformers resolve entity type ambiguities that static features cannot.
• Relation extraction identifies semantic relationships between entity pairs, producing (subject, predicate, object) triples suitable for knowledge graphs. The pipeline approach applies a relation classifier to entity pairs identified by NER, with entity type filtering to reduce the candidate pair space. Distant supervision enables training at scale without expensive manual annotation.
• Event extraction captures structured descriptions of occurrences, including the event trigger, type, and argument roles. It is the most complex IE task because a single sentence may contain multiple events with shared arguments, triggers are ambiguous, and arguments can be implicit or spread across sentences.
• Structured output generation uses LLMs with precise schema-defining prompts to extract complete, schema-compliant JSON records in a single pass. JSON Schema validation and function calling APIs enforce output structure. Attribution-based extraction, where each value is paired with its source span, mitigates the hallucination risk.

The choice of approach depends on your requirements. Rule-based and traditional ML systems offer interpretability and low latency but require domain-specific engineering. Fine-tuned transformer models achieve the best accuracy on well-defined tasks with annotated training data. LLM-based extraction is fastest to adapt to new schemas and domains, at the cost of higher inference cost and hallucination risk. In practice, production IE systems often combine all three: LLMs for initial schema exploration and low-volume complex extraction, fine-tuned models for high-volume extraction, and rule-based post-processing to enforce hard constraints on both. The key evaluation metric across all approaches is entity-level F1, which gives no partial credit and therefore accurately reflects the all-or-nothing nature of most downstream applications.