Word2vec is pretty good at comparing "subjects", e.g.:
$$\langle dog, pup \rangle \simeq 0.81$$
$$\langle pants, trousers \rangle \simeq 0.75$$
But it appears to be not great at encoding "relationships" in a way that preserves semantics, e.g.:
$$\langle spouse, wife \rangle \simeq 0.52$$
$$\langle employer, boss \rangle \simeq 0.27$$
$$\langle own, posess \rangle \simeq 0.26 $$
- Is there any obvious intuition for why this is the case?
- Is there any well-known way to encoding these relationship-type words in a way that the cosine distance would actually successfully measure semantic similarity?
 I computed these examples with the webapp here