Named Entity Recognition (NER)
An NLP technique that identifies and classifies named entities — people, organizations, locations, dates, amounts — in text.
NER models are trained to identify spans of text that refer to specific entity types and classify them into predefined categories. In general NER, common categories include persons (PER), organizations (ORG), locations (LOC), dates (DATE), and monetary values (MONEY). Domain-specific NER models are trained for specialized entity types: legal NER identifies contract parties, clause types, and legal concepts; financial NER identifies company names, financial metrics, and regulatory references.
NER is foundational to document intelligence pipelines. Before a system can answer "who are the parties to this contract?" or "what are the payment amounts?" it must first identify where party names and monetary values appear in the document. Modern transformer-based NER achieves high accuracy on well-structured documents like contracts and filings, though accuracy degrades on scanned documents, handwritten content, or non-standard formatting.
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Large Language Model (LLM)
A neural network trained on massive text corpora that can understand and generate human language.
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