Natural Language Processing
The branch of AI that deals with the interaction between computers and human language. NLP enables machines to read, understand, generate, and make sense of human language in a useful way.
Why It Matters
NLP powers chatbots, translation services, sentiment analysis, and search engines. It is the technology that makes human-computer conversation possible.
Example
Google Translate converting a paragraph from English to Japanese, or Gmail suggesting quick replies to your emails.
Think of it like...
Like having a multilingual interpreter who not only translates words but understands context, tone, sarcasm, and cultural nuances.
Related Terms
Tokenization
The process of breaking text into smaller units (tokens) for processing by NLP models. Tokenization can split text into words, subwords, or characters depending on the method used.
Named Entity Recognition
The NLP task of identifying and classifying named entities in text into predefined categories such as person names, organizations, locations, dates, monetary values, and more.
Sentiment Analysis
The NLP task of identifying and classifying the emotional tone or opinion expressed in text as positive, negative, or neutral. Advanced systems detect nuanced emotions like frustration, excitement, or sarcasm.
Large Language Model
A type of AI model trained on massive amounts of text data that can understand and generate human-like text. LLMs use transformer architecture and typically have billions of parameters, enabling them to perform a wide range of language tasks.