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NLP sits at the intersection of computer science, artificial intelligence, and linguistics. Its goal is to allow machines to process massive amounts of natural language data. Modern NLP has moved away from strict rules-based processing towards statistical and deep learning models.
A branch of artificial intelligence focused on enabling computers to understand, interpret, and generate human language.
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Historically, NLP relied on recurrent neural networks (RNNs) and LSTMs that processed text sequentially. The introduction of the Transformer architecture in 2017 reshaped NLP by allowing models to process all words in a sentence simultaneously and understand long-range context through attention mechanisms.
Yes. LLMs represent the current state-of-the-art in NLP. While early NLP systems focused on specific tasks like sentiment analysis or translation, modern LLMs are general-purpose reasoners that can perform almost any language task.
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