Neural Networks for Language: How Context Became a Learned Transformation

Once language modeling moved beyond pure counting, something important changed. The question was no longer only: “How often did these words appear together before?” It became: “Can a model learn what kinds of words tend to fit together, even when it has not seen the exact phrase many times?” That was a major turning point. N-grams had already shown that next-word prediction could be framed as a probability problem. But they were still brittle. They did not really learn language in a deeper sense. Early neural language models were the first serious attempt to change that.