0
$\begingroup$

In "Efficient Estimation of Word Representations in Vector Space" Mikolov et.al argue that any mapping of words into vectors should satisfy approximate constraints, such as

$vector(''Paris'') - vector(''France'') + vector(''Poland'') \approx vector(''Warsaw'')$

Then, on page 5 they choose the Cosine distance to measure the vector proximity. Unfortunately, cosine distance is not a metric. It hints however that perhaps the problem setup has to be transferred into Euclidean Projective space. Unfortunately, there is no identity element in projective space. Why the authors didn't use a conventional norm, such as $l^2$?

$\endgroup$

0

Your Answer

By clicking “Post Your Answer”, you agree to our terms of service and acknowledge you have read our privacy policy.

Browse other questions tagged or ask your own question.