What is information extraction? Locate source fields rather than invent values
Explain entity and date extraction, separating structure from evidence and checking unknowns, positions and relationships.
Understand the concept
Information extraction identifies facts and organizes fields, entities or relations from existing material. Rules or models can do it, but outputs need source traceability. A valid date can belong to the wrong event and missing facts must not be invented. Validate meaning, location and relationships, not just JSON or full cells.
Field structure and factual support need separate checks
Document analysis returns fields and layout while structured models constrain organization. Values can come from examples or neighboring rows. Define semantics and unknowns and retain sentences or coordinates. Rules handle stable syntax and models handle phrasing, but both need negation and condition checks. Planned and actual dates must not be merged under one valid format.
Test missing and conflicting fields in the same material
Extract public event times and locations, test missing locations and canceled original dates. Preserve source-output comparisons instead of self-grading. No model or real document processing occurred here. Multi-record cases also need checks against values crossing objects; matching words do not prove attribution.
Keep downstream semantics aligned with extraction rules
Replay samples after field or template changes and preserve unknown semantics and references. Validate before automated writes instead of turning guesses into facts. Count review and corrections rather than parse successes. Combine methods with processing and source versions. Deliver traceable values that can be corrected, not merely a neat table detached from originals.
Checked sources
These sources support the concepts and methods described here. Examples and test scope are labeled separately; check the version of the tools you use.