John, (01)
For full understanding your point 2 below probably has to be read in
context of your broader point about the failure of standards. But I
can read a lot of what I want into it. (02)
On Mon, Feb 1, 2010 at 4:44 AM, John F. Sowa <sowa@xxxxxxxxxxx> wrote:
> ...
> Conclusions:
>
> ...
>
> 2. For large, broad coverage of any significant amount of language,
> a great deal of useful information can be derived *automatically*
> from dictionaries, encyclopedias (including Wikipedia), and other
> documents written for human consumption.
>
> 3. ... (03)
In large part my posts come down to encouraging exactly this approach:
that the best way to model knowledge is by directly processing
patterns found in raw text. (04)
My discussions of Turning's halting problem/decidability/Wolfram's
computational irreducibility just give a theoretical justification why
this should be so. (05)
With or without theoretical justification, such ad-hoc search for
patterns is becoming the standard knowledge representation paradigm
anyway. Compare wired editor Chris Anderson's "End of Theory" article
a couple of years back: (06)
'Speaking at the O'Reilly Emerging Technology Conference this past
March, Peter Norvig, Google's research director, offered an update to
George Box's maxim: "All models are wrong, and increasingly you can
succeed without them."' (07)
http://www.wired.com/science/discoveries/magazine/16-07/pb_theory (08)
Google's indexed search (enhanced with a little "link mining") is the
standard knowledge access paradigm today. (09)
But from this perspective your approach still seems limited. You seem
to see that it is best to work directly with text, but seek to return
to abstract representations as soon as possible. (010)
Perhaps this is just an impression. Have you at least moved away from
the position that some kind of abstract meaning representation is
necessary for "meaning-related" language processing problems like
speech recognition? (011)
-Rob (012)
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