I don't know. The problem here might be human expectations. As the article says, word2vec sometimes is "sold" with these kinds of analogies that seem so intuiti

I don't know. The problem here might be human expectations. As the article says, word2vec sometimes is "sold" with these kinds of analogies that seem so intuitive. So we come to expect the system to behave intuitively. And in many cases it does. But, what are you really doing there? Well, you are operating with vectors. And what do those vectors represent? Not the semantics about the words themselves. The vectors encode the context in which they appear. They encode what other words tend to surround them. And there are more limitations. The most obvious one is polysemy. But even without polysemy, different words might have more or less "diffuse" representations. The more technical, precise and uncommon a term is, the less "diffuse" its vector is. King and queen, man and woman are far from precise, technical or uncommon terms. And therefore, there's a lot of "diffusion". Again, the problem here is our expectations versus the results we see in practice. But the models are what they are. And they are not magic.