Research
Information Extraction
How entities, relationships, facts, and evidence are extracted from messy business records and technical documents.
Information extraction is the bridge between raw records and useful operating models. The goal is not just to extract text; it is to preserve the entity, relationship, source, and confidence needed to act on it.
Core Questions
- Which entities and relationships matter for finance, compliance, customer work, and follow-up?
- How should extraction systems expose confidence, ambiguity, and missing evidence?
- When should extraction use schemas, graphs, language models, rules, or human review?
Where It Shows Up
Application
Structured Records
Turn invoices, contracts, statements, emails, and notes into structured records.
Application
Evidence Links
Attach source spans and evidence links to extracted facts.
Application
Reviewable Inputs
Create reviewable inputs for planning, compliance, and advisory workflows.
Artifacts
- InfoExtract utilities.
- GraphForge graph construction experiments.
- Community reads on relation extraction and text-to-graph methods.