NLP Information Extraction — Structured Data From Unstructured

I will build an NLP information extraction pipeline for your text data — covering named entity recognition, relation extraction (identifying how entities relate to each other), event extraction, and co-reference resolution — delivering structured, database-ready output from your unstructured text documents. Information extraction is the technology underlying every system that converts unstructured text into structured knowledge — contract analysis tools that extract party names and obligation relationships, clinical systems that extract diagnoses and treatments from notes, and financial systems that extract company names and monetary figures from reports all rely on the same extraction pipeline components.

The pipeline covers NER for your entity types, relation extraction identifying relationships between entities, event extraction for structured event representations, co-reference resolution linking pronoun references to entities, output formatting to your database schema, and batch processing for your document volume.