Information Extraction
The automated process of identifying and pulling structured data from unstructured text documents.
Information extraction encompasses several NLP tasks: named entity recognition (identifying people, organizations, dates, amounts), relation extraction (understanding how entities relate to each other), event extraction (identifying what happened and when), and template filling (populating structured forms from free text). Together, these techniques transform document text into data that can be queried, compared, and analyzed programmatically.
In document intelligence applications, information extraction is what allows a platform to read a contract and produce a structured output: parties, effective date, term, payment amounts, termination triggers, governing law. This structured output can then be compared across hundreds of contracts simultaneously, analyzed for patterns, or exported for downstream processing in contract management or compliance systems. The accuracy of extraction — particularly for complex, nested provisions — determines the practical reliability of the downstream workflows.
Related Terms
More ai/ml Terms
Retrieval-Augmented Generation (RAG)
An AI architecture that combines information retrieval with text generation to produce answers grounded in source documents.
Vector Embedding
A numerical representation of text as a high-dimensional vector, enabling semantic similarity comparisons between passages.
BM25
A probabilistic keyword-ranking algorithm that scores documents by term frequency and inverse document frequency.
Chunking
The process of splitting large documents into smaller, overlapping segments optimized for retrieval and embedding.
Hallucination
When an AI model generates plausible-sounding but factually incorrect or fabricated information.
Large Language Model (LLM)
A neural network trained on massive text corpora that can understand and generate human language.
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