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NLP, or Natural Language Processing, is a branch of artificial intelligence (AI) and computational linguistics that focuses on the interaction between computers and human language. It involves developing algorithms and models to enable computers to understand, interpret, and generate human language in a way that is both meaningful and useful.

Language is a fundamental aspect of human communication and understanding. It encompasses not only spoken and written words but also the context, semantics, syntax, and pragmatics that give meaning to those words. NLP aims to bridge the gap between human language and machine language by enabling computers to process and analyze natural language data.

The field of NLP has evolved significantly over the years, driven by advancements in machine learning, deep learning, and linguistic theories. Early approaches to NLP focused on rule-based systems, where experts manually defined a set of rules to extract information or perform specific language-related tasks. However, these systems were limited in their ability to handle the complexity and variability of natural language.

The advent of machine learning and statistical methods revolutionized NLP by allowing computers to learn patterns and relationships from large amounts of labeled language data. This data-driven approach, known as supervised learning, involves training models on annotated datasets and leveraging statistical techniques to make predictions or perform tasks such as text classification, named entity recognition, sentiment analysis, and machine translation.