Natural language processing is a branch of AI that enables machines to understand, interpret, and generate human language. It combines linguistics, statistics, and machine learning to process text and speech, allowing systems to go beyond recognizing words and instead grasp context, intent, and meaning.
NLP tasks generally fall into two categories. Low-level tasks handle the structural side of language, such as breaking text into individual words (tokenization) or labeling parts of speech. High-level tasks deal with meaning and communication, including
- Text classification: Organizing and categorizing text, used in sentiment analysis, spam detection, and language detection.
- Information extraction: Pulling structured data from unstructured text, such as names, dates, or locations.
- Machine translation: Converting text from one language to another.
- Question answering: Enabling systems to respond to natural language queries, as chatbots do.
Early NLP systems relied on manually written rules, which worked for simple, well-defined tasks but broke down quickly when language varied. Machine learning improved this by automatically finding patterns in data, though it still required significant feature engineering.
Deep learning pushed NLP further by using neural networks that learn directly from raw text, eliminating much of that manual preparation. The attention mechanism and transformer models were particularly significant advances, giving models the ability to weigh the importance of different words in a sequence and handle long, complex inputs more effectively.
Today, NLP is central to large language models, named entity recognition (NER), retrieval augmented generation (RAG), and multimodal models that process both text and other data types.