Natural Language Processing

Short definition

Natural language processing is the area of AI that deals with human language. It includes tasks such as translation, summarization, sentiment analysis, intent detection, question answering and text generation.

How it works

Older NLP systems often relied on rules and handcrafted features. Modern NLP usually uses deep learning models trained on large text datasets. These models convert language into numerical representations and learn patterns that help them process meaning and context.

Example

A search engine can use NLP to understand that a query about cancelling a subscription is related to billing support, even if the user does not use the exact words from the help article.

Why it matters

NLP is central to chatbots, assistants, search, content analysis and enterprise knowledge tools. It makes software easier to use because people can interact with systems in normal language instead of rigid forms or commands.

NLP is not complete understanding

A model may classify a sentence correctly or produce a convincing answer while still missing intent, irony, negation or industry context. Lower-resource languages, dialects, abbreviations and messages that mix languages are particularly challenging.

How to evaluate an NLP system

The metric should match the task. Classification may use precision and recall, search needs ranking relevance, and generated text needs factual and usefulness review. Break down results by language, text length and user group because a good overall average can hide poor performance for a specific audience.