Matt: Semantic models learn associations by proximity in the training text.
The
degree to which you associate "snake" and "rope" depends on how often these
words appear near each other
Correct me - but it's the old, old problem here, isn't it? Those semantic
models/programs won't be able to form any *new* analogies, will they? Or
understand newly minted analogies in texts? And I'm v. dubious about their
powers to even form valid associations of much value in the ways you
describe from existing texts.
You're saying that there's a semantic model/program that can answer, if
asked,:
"yes - 'snake, chain, rope, spaghetti strand' is a legitimate/ valid series
of associations"/ "yes, they fit together" (based on previous textual
analysis) ?
or: " the odd one out in 'snake/ chain/ cigarette/ rope" is 'cigarette'"?
I have yet to find or be given a single useful analogy drawn by computers
(despite asking many times). The only kind of analogy I can remember here is
Ed, I think, pointing to Hofstader's analogies along the lines of "xxyy"
is like "xxxxyyyy". Not exactly a big deal. No doubt there must be more,
but my impression is that in general computers are still pathetic here.
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