On Mon, Mar 27, 2017 at 10:12 AM, Ben Goertzel <[email protected]> wrote:
> Now, the distinction between word2vec and sense2vec may indeed be
> relevant here, because a word may be meaningfully be placed into two
> categories at this stage (i.e. word sense disambiguation).   So we may
> want to do something like they describe in this paper
>
> https://pdfs.semanticscholar.org/142f/38642629b9d268999ad876af482177d36697.pdf
>
> which is similar to sense2vec but slower to run, but has the advantage
> of (unlike sense2vec) not requiring labeled training examples

Code corresponding to the above paper is here:

https://github.com/ninjin/huang_et_al_2012

A simpler, faster (but maybe worse) approach to the same problem is
embodied in this code

https://github.com/nishantrai18/cs671project

The latter simpler code would be "easy" to apply to the current
context, because it's based on postprocessing word2vec vectors, so I
suppose one could apply it to postprocessing word2vec-type vectors
obtained from "disjunct vectors" that are produced from ensembles of
parse trees...

-- Ben



-- 
Ben Goertzel, PhD
http://goertzel.org

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