Monosemous word vectors sit at the intersection of their context subspaces

Mu, Bhat & Viswanath fit a low-rank PCA subspace to each of many contexts around a target word in 300-dim word2vec skip-gram embeddings (Wikipedia) [mu-etal-2016-geometry-of-polysemy] For monosemous words, cosine similarity to the many context subspaces is far above a random-context baseline (e.g. "typhoon" 0.693 vs 0.305) [mu-etal-2016-geometry-of-polysemy] A word's vector thus occupies the geometric intersection of its senses' low-rank context regions, used downstream for sense clustering (K-Grassmeans) [mu-etal-2016-geometry-of-polysemy] No causal intervention is performed [mu-etal-2016-geometry-of-polysemy]