Information Extraction

Information Extraction is the NLP task of converting unstructured text into structured facts—entities, relationships, dates, prices. A pipeline tokenizes input, tags spans with a model such as BiLSTM-CRF or a Transformer, then outputs JSON triples like {“company”: “OpenAI”, “funding_round”: “Series A”, “amount”: “$100 M”}. Advanced setups add relation extraction, coreference resolution, and normalization to knowledge-graph IDs. Precision, recall, and F1 on annotated corpora gauge quality. Use cases include contract analytics, news monitoring, and seeding vector stores for Retrieval-Augmented Generation. Challenges—domain drift, ambiguity, privacy—are eased with weak supervision or active learning. By turning free-form prose into queryable data, Information Extraction fuels search, BI dashboards, and downstream AI workflows.