Information Extraction
Information extraction converts source content into structured entities, attributes, relations or events. Practitioners define a schema, connect outputs to evidence and evaluate both missing information and unsupported fields. The competence is broader than recognizing names, and differs from question answering because it produces a reusable structured representation under an extraction contract.
What it is
An extractor identifies information in text and maps it into a structure such as a record, relation tuple or event with arguments. Schema-based extraction uses predefined fields or relation types; open information extraction seeks more flexible predicate-argument relations. Systems can combine patterns, linguistic analysis, span models and generative structured outputs. Normalization may convert dates or units, while linking maps mentions to canonical records. These are additional operations whose correctness must be checked. Missing and contradictory evidence require explicit representation. Extracting a sentence's claim does not verify that the claim is true, and a well-formed record does not prove that every value is supported by the source.
What the work involves
Write field definitions, evidence requirements and policies for absent, conflicting or multiple values. Annotate representative documents and audit agreement at field and relation level. Keep source families and duplicate records together across splits. Compare rules with learned extraction, preserve source spans or other evidence links and validate output types and relationships. Measure precision and recall for fields, arguments and complete records, including unsupported-value rates. Inspect normalization and entity linking separately. The useful result is a versioned extraction contract and pipeline whose structured outputs can be traced back to the text and reviewed when evidence is insufficient.
Illustrative example
An illustrative system extracts maintenance events from reports: device, fault, action and completion date. The text describes an inspection scheduled for next week, so the correct record distinguishes a planned action from a completed one. The developer checks source spans and relation roles, not just whether the date and device name were found. Held-out reports include contradictions and multiple devices. Missing fields remain empty with evidence status instead of being filled from the model's general knowledge.
Limits and common mistakes
Valid JSON can contain incorrect or fabricated values. Recognizing every entity still does not establish their relationships, and nearby text may describe different events. Normalization can lose ambiguity, while extraction from tables or scans also depends on upstream parsing or OCR. OCR recognizes characters and is only one possible input stage. Distinguish extraction, entity recognition, linking and factual verification. Evaluate complete structured meaning and evidence support, including absent and contradictory information, rather than relying on schema validity alone.
Prerequisites
- mediumNLP
Extraction depends on language representation, span handling and linguistic ambiguity.
Related skills
- → is subcategory of: NLP
Sources and further reading
- Stanford Open Information Extraction
Official predicate-argument extraction interface and output semantics.
- Leveraging Linguistic Structure For Open Domain Information Extraction
Primary method connecting linguistic structure to extracted relation tuples.
Last updated: 2026-10-10