Natural Language Processing

Natural Language Processing (NLP) is a field at the intersection of computer science, artificial intelligence, and linguistics that focuses on enabling machines to work with human language. It encompasses tasks ranging from basic text processing like tokenization and part-of-speech tagging to complex challenges like machine translation, sentiment analysis, question answering, and open-ended text generation.

The field has undergone dramatic shifts in methodology. Early NLP relied on hand-crafted rules and grammars. Statistical methods using techniques like TF-IDF and n-gram models followed. The deep learning era introduced recurrent neural networks and word embeddings that could capture semantic meaning. The transformer architecture, introduced in 2017, revolutionized the field by enabling models to process entire sequences in parallel through self-attention, leading directly to the large language models that now dominate NLP.

Modern large language models like GPT, Claude, and Gemini have effectively unified most traditional NLP tasks into a single paradigm: given a text prompt, generate an appropriate text response. Tasks that previously required specialized models, separate training pipelines, and custom architectures can now be handled by a single foundation model through prompt engineering. This consolidation has shifted NLP research toward understanding model behavior, improving reasoning capabilities, reducing hallucinations, and ensuring safety, rather than building task-specific systems from scratch.