One of the things you can do with co-occurrence model word representations is subtract or add them, to see what the resultant word representation 'means' (I skip over the mathematical details since we are just here for fun). For example, in word2vec space:
king - man + woman = queenThe equality sign here has to be taken with a grain of salt; it really means 'is similar to'.
My colleague Geoff Hollis and I have been working with the word2vec model (using a smaller dictionary and a slightly different representation and similarity measure than Google). I added the ability to add fractions of representations instead of just adding or subtracting each word representation as a whole, and have spent some time looking for interesting semantic math results. I have defined '=' here as 'being in the top ten closest results' (and also restricted myself by requiring that the final result on the right of the '=' sign cannot be among the top ten closest neighbors of any the input words on the left of that sign). This human flexibility (and the fact that I have deliberately searched for interesting results) means that this math is really a human-computer collaboration rather than a purely computational result.
Here are some of my most interesting results. Enjoy.
- love + 0.4 * sex = friendship
love + sex = infidelity
love + 3 * sex = monogamy
murder + fun = gunplay
apple + pig = potato
cat + 0.7 * dog = poodle
despair + 0.5 * hope = frustration
wealth + 0.2 * dream + 3 * selfish = elitist
courage + 2 * stupidity - incompetence = audacity
hope + time = opportunity
logic + hope = principle
man - 2 * education = snake
tiger - cat = rhino
sex + drunken = debauchery
love + dream = passion