Word Embedding
A foundational NLP technique that maps individual words into a dense mathematical vector space where geometric distance represents semantic similarity.
Think of It Like This
Like placing cities on a map; New York and Boston are placed geographically close together, while Tokyo is mapped far away.
Because neural networks only process numbers, words must be translated into vectors. In this multi-dimensional space, mathematical operations capture language meaning—famously, 'King - Man + Woman = Queen'. These dense, continuous representations replaced massive, inefficient one-hot encodings and formed the absolute bedrock of modern language modeling.