I agree disambiguation is a tough problem, but I'm curious to see whether the vector representation learned by word2vec can help in that regard. Have you consid

I agree disambiguation is a tough problem, but I'm curious to see whether the vector representation learned by word2vec can help in that regard. Have you considered using clustering to attach a few possible cluster memberships to each word (which would hopefully each capture information about a different usage context), then selecting the meanings for each word that give the maximum overlap? The idea being that the words used to search on your website are likely to be conceptually similar (one doesn't add apples to oranges), so you can assume that "queen (sovereign) + man (noun) - woman" makes more sense that "queen (band) + man (unix command) - woman" because "man (noun)" and "woman" are likely to belong to similar clusters and not "man (unix command)".