What is Natural Language Processing (NLP)?
Natural Language Processing is the branch of artificial intelligence focused on giving computers the ability to understand, interpret, and generate human language.
Natural Language Processing is the branch of artificial intelligence focused on giving computers the ability to understand, interpret, and generate human language. NLP powers chatbots, search engines, translation services, sentiment analysis, content moderation, and text summarization.
Modern NLP is dominated by transformer-based language models. Before transformers (pre-2017), NLP relied on statistical methods, word embeddings (Word2Vec, GloVe), and recurrent neural networks. Post-transformers, pre-trained models like BERT (understanding) and GPT (generation) transformed the field.
Key NLP tasks include: text classification (spam detection, sentiment analysis), named entity recognition (extracting people, companies, dates from text), machine translation, question answering, summarization, and text generation.
For business applications, NLP enables: automated customer support, document analysis, contract review, compliance monitoring, market intelligence, and content generation. The economics of NLP applications depend heavily on model choice — smaller task-specific models are dramatically cheaper than general-purpose LLMs.
Why It Matters
NLP is the technology that makes AI accessible to non-technical users through natural language interfaces. Understanding NLP capabilities and limitations is essential for any executive evaluating AI investments.
Frequently Asked Questions
What is NLP?
Natural Language Processing is AI that understands and generates human language. It powers chatbots, search engines, translation, sentiment analysis, and content generation.
What is the difference between NLP and LLMs?
NLP is the broad field of language AI. LLMs are a specific type of NLP model (large transformer models). Not all NLP uses LLMs — many tasks use smaller, cheaper, task-specific models.
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Richard Ewing is a Product Economist and AI Capital Auditor. He helps companies translate technical complexity into financial clarity.
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