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Natural Language Processing (NLP) is the branch of AI that enables machines to understand, interpret, and generate human language. It's what powers translation apps, voice assistants, chatbots, spam filters, and search engines.
Early NLP used rule-based systems: programmers wrote grammatical rules and vocabulary. This was brittle — real language is full of ambiguity, idioms, regional variations, and context-dependence that rules can't fully capture.
The shift to statistical and then deep learning approaches changed everything. Instead of rules, models learn patterns from massive text datasets. The transformer architecture (2017) was the breakthrough enabling today's LLMs.
Key NLP tasks: sentiment analysis (is this review positive or negative?), named entity recognition (identifying people, places, organisations in text), machine translation, text summarisation, question answering, and text generation.
For African languages, NLP is significantly less developed than for English and major European languages. There's less training data available in Yoruba, Swahili, Twi, and Amharic, which limits model performance. Efforts like Masakhane — a grassroots research initiative — are specifically working on NLP for African languages.
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