That mainly depends on the number of dimensions on the model, and if the training picked up split meaning. With a high dimension vector (>50d, more usually 300

That mainly depends on the number of dimensions on the model, and if the training picked up split meaning. With a high dimension vector (>50d, more usually 300d or 500d) one or two dimensions together will pick up a concept. This is why word algebra like Paris - France + Russia ~ Moscow works with original Word2Vec vectors. Language models are better at picking up context-dependent meaning, but word vectors still have good enough performance for many tasks.