4 ms·

Good point! I would see this rather as yet another argument for why you should simply give the actual output of the NLP algorithm. So if people actually do the

• feanaro 7y ago
  In the end, it doesn't matter what transformation you do though, as long as you do it consistently and/or not in an ad hoc manner. If excluding the original term always leads to useful results, it is a useful transformation. The problems materialize when you're just cherry picking for results.
• Sean1708 7y ago
  To be honest, I think the idea that we should expect ML algorithms to give a single, certain answer is misguided. I would expect the output from this algorithm to be "King - Man + Woman = King (90%), Queen (83%), Prince (70%)" or something like that, i.e. a list of answers with some measure of how "good" those answers are. Then again, I work in a field that doesn't really have categorical answers so maybe I'm missing something obvious.
  • piker 7y ago
    But the example is only performing vector operations. You could perhaps normalize the distances of a number of vectors with a softmax or something to produce a probability across a set, but what's being presented in the paper is the "closest" vector following the operations in terms of cosine distance.
  • flo_hu 7y ago
    That's pretty much correct. You would typically calculate a vector for "King-Man+Woman" and then do a query on this based on a cosine distance (or similar measure) over the entire vocabulary. The query would give you a ranked list of the closest word vectors with scores that indicate how good the match is.