The semantic vector of a sentence is approximately the sum of the word
vectors, not the product. It is not exact because it does not account
for word order. John + loves + Mary = Mary + loves + John.

On Wed, Jun 18, 2014 at 8:47 AM, YKY (Yan King Yin, 甄景贤)
<[email protected]> wrote:
>
> Words or concepts can be extracted as vectors using Google's word2vec
> algorithm:
> https://code.google.com/p/word2vec/
>
> To express a complex thought composed of simpler concepts, a mathematically
> natural way is to multiply them together, for example "John loves Mary" =
> john x loves x mary.
>
> I'm wondering if forming the tensor products from word2vec vectors could be
> meaningful.
>
> The tensor product is a bi-linear form (the most universal such bi-linear
> mappings).  So it may preserve the linearity of the original vector space
> (in other words, the scalar multiplication in the original vector space).
> If the scalar multiplication is meaningful in the word2vec space, then its
> meaning would be preserved by the tensor product.
>
> The dimension of the tensor product space is also much higher (as the
> product of the dimensions of the original spaces;  this is even greater than
> the Cartesian product which is the sum of the dimensions of the original
> spaces.)  Computationally, I wonder what is the advantage of using tensor
> products as opposed to Cartesian products...?
>
> Or perhaps the extra richness of tensor structure can be exploited
> differently...
>
> --
> YKY
> "The ultimate goal of mathematics is to eliminate any need for intelligent
> thought" -- Alfred North Whitehead
>
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-- 
-- Matt Mahoney, [email protected]


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