3 ms·
After putting some documents in word2vec, it gives you a vector representation of each word (1). Words that are close to each other in this space might be simil
by quonn 10y ago
After putting some documents in word2vec, it gives you a vector representation of each word (1). Words that are close to each other in this space might be similar, for example. Famously, it's possible to do neat computations, e.g. king - man might result in a vector that's close to the one for queen.
(1) This representation is learned, essentially, by trying to predict words from the surrounding words in a sentence or the other word around.
- versteegen 10y agoYou need to compute king - man + woman, since, in a well learned model, the king - queen vector is similar to the man - woman vector. These gender-based vector differences can be explained as capturing syntactic rather than semantic information, but semantic relations (Rome:Italy -> Paris:France) can also work.