distributional-semantics-foundation-embeddings

Summary

All the popular word and sentence embedding models, from Word2Vec all the way through BERT and modern transformers, rest on the same core idea: a word's meaning is defined by the words and sentences it shows up near. This matters because it means the system can treat very different embedding architectures as variations on one shared principle, rather than as unrelated technologies, which simplifies reasoning about which models can be swapped or compared for a given task.