Natural Language Processing (NLP)
A field of AI focused on enabling computers to understand, interpret, and generate human language.
NLP encompasses a wide range of tasks including text classification, named entity recognition, sentiment analysis, machine translation, and question answering. Modern NLP is dominated by transformer-based models that learn language patterns from large corpora.
Document intelligence relies heavily on NLP for text extraction, section detection, entity recognition, and answer generation. These capabilities allow platforms to transform unstructured documents into searchable, queryable knowledge bases.
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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