Tag

#NaturalLanguageProcessing

Natural language processing is the field that taught computers to read and, more recently, to write, and these posts track it from its linguistic roots to its current statistical dominance. I write about the shift from rule-based systems to embeddings to transformers, what was gained and what was lost when the field stopped trying to model grammar and started modeling distribution, and the persistent gap between benchmark performance and real-world usefulness. The pieces here cover the practical, tokenization, entity extraction, translation, and the theoretical, what it means that a model can predict the next token without understanding a single word. NLP is where language and computation collide, and the collision is still producing more questions than answers.