Hi Ben & Linas,

I assume you're familiar with sense2vec. At the one paragraph level of
detail, this suggestion seems pretty similar to me, any key differences?

On Sun, Mar 26, 2017 at 1:44 PM, Ben Goertzel <b...@goertzel.org> wrote:

> Linas,
>
> I thought a bit about how to use a modified version of the word2vec
> idea in our language learning pipeline...
>
> I'm thinking about the Skip-gram model of word2vec, as summarized
> informally e.g. here
>
> http://mccormickml.com/2016/04/19/word2vec-tutorial-the-skip-gram-model/
>
> Following up the suggestion you made in Addis in our chat with
> Masresha, I'm thinking to replace the "adjacent word-pairs" used in
> word2vec with "word-pairs that are adjacent in the parse tree" (where
> e.g. the parse tree may be the max-weight spanning tree in our
> language learning algorithm)....
>
> This would still produce a vector just like word2vec does, via the
> hidden layer of the NN ... but the vector would likely be more
> meaningful than a typical word2vec vector...
>
> What would the purpose of this be, in the context of our language
> learning algorithm?  The purpose would be that clustering should work
> better on the word2vec vectors than on the raw-er data regarding "word
> co-occurrence in parse trees."   At least, that seems plausible, since
> clustering on word2vec vectors generally works better than on
> co-occurrence vectors
>
> This would be something that Masresha or someone else in Addis could
> work on, I think...
>
> We can discuss at the office this week...
>
> ben
>
>
> --
> Ben Goertzel, PhD
> http://goertzel.org
>
> “Our first mothers and fathers … were endowed with intelligence; they
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>
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> gods? … What if they do not reproduce and multiply?’
>
> Then the Heart of Heaven blew mist into their eyes, which clouded
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>
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