Fascinating. Thanks for all the definitions. (01)
Wouldn't linking background knowledge together eventually simplify
what actually are persistent ambiguities versus problems that may
already be addressed by others, or have more concise definitions in
other languages? (IE no one-word English translation for the German
word 'gestalt') (02)
Instead of languages, take an open question in the field of physics
for example. Today there are so many specialized areas of physics and
these methods of thinking, working, communicating, documenting and
exchanging ideas and information are practiced all over the world. But
the further back in time you go, less was known, there weren't as many
labs or publications, or literally as many words and equations to keep
track of or provide. At some point, every background knowledge trail
will end because a particular ambiguity had not been imagined or
recorded yet. Either a person or computer, or network of people and
machines, notices a new ambiguity and they are already limited in
saying where the ambiguity belongs in terms of background knowledge
because the whole issue is new. Words may have been invented to
describe it, this act itself tells a story. (03)
But I imagine your objective is not to make a clear picture of what we
knew before, reinvent citation methods, or even show how we advanced
from star gazing to modern astronomy - but to connect enough accurate
knowledge being exchanged via computers and networks to see - oh look,
this ambiguity described by these words is currently being
passionately argued between XYand Z labs, published in these journals,
the heart of the matter is expressed by these equations, this is where
the situation currently stands. A machine identifying this ambiguious
term would then open up these particular floodgates using a mechanical
version of common sense and collective memory/experience? Is your
purpose to limit the *LEVELS* being searched and point towards the
particular resources involved in the difficulties surrounding
ambigious terms? (04)
Wouldn't it be great to be fed updates as the ambiguity is removed by
being solved or getting splintered into solved versus unsolved
portions of the problem? (05)
Debbie (06)
On 2/21/07, John F. Sowa <sowa@xxxxxxxxxxx> wrote:
> Debbie,
>
> Re NLP: NL is a common abbreviation for natural language,
> and NLP is natural language processing. Many people (including
> me) have been working on NLP for years, and we have all learned
> that the task is much harder than anyone had anticipated.
>
> > In highly structured documents and words such as a set
> > of specifications, don't you think identifying ambiguities
> > people do not notice is a different task than looking for
> > and highlighting ambiguities that require interpretation?
>
> Yes, they are different tasks. But the ambiguities that
> must be resolved for people (who use "commonsense" to
> interpret documents) are just a small subset of the problems
> that a computer runs into (because it doesn't have commonsense).
>
> > Is the purpose of auto-nitpicking (what is supposed to be)
> > structured text to pull out seemingly minor inconsistencies
> > so they can be either dismissed or decided?
>
> If we want to get computers to help us process ordinary
> language, we have to provide them with the kind of background
> knowledge (i.e., "common sense") that people use. But we have
> to teach them that background knowledge by using more structured
> languages that they are capable of interpreting right now --
> examples include symbolic logic or programming languages.
>
> The hope is that if we provide enough background knowledge,
> then perhaps someday they would be able to read the same
> kinds of texts that are designed for humans to read. Some
> people (such as Doug Lenat and the Cyc project) have been
> trying to do that for the past 22 years, and they still
> haven't succeeded (or Lenat would say that they have only
> achieved partial success).
>
> John
>
>
>
>
>
>
>
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> (07)
-- (08)
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Deborah MacPherson
www.accuracyandaesthetics.com
www.deborahmacpherson.com (010)
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