On 12/9/2011 4:56 PM, doug foxvog wrote:
> I do not know if the referenced phrase has been encoded in CycL, but it
> could be encoded as follows:
>
> (multiWordString Have-TheWord "it up to here" Verb
> (VeryHighAmountFn Frustration))
>
> The Cyc NLP has transformations that map
> "<Instance of Agent> <HAVE> <Emotion>"
> to the predicate feelsEmotion, such that it would generate
>
> (feelsEmotion ?SPEAKER (VeryHighAmountFn Frustrati (01)
That's a good example of how "frozen metaphors" can be
represented. As I said in my previous note, there are
many special case techniques that work very well. (02)
But developing a good, general, flexible method that can
recognize, interpret, and represent all metaphors is still
a major research problem. (03)
But we can still do a lot of NLP without solving every
problem. Even human beings vary widely in their ability
to understand their native language. (04)
In fact, nobody can understand every document, even in their
native language, without using a lot of reference material
or asking help from teachers and experts in the field of
application. (05)
A good goal is to develop computer systems to a level where
they can interpret a lot of language, recognize what they
don't understand, and use reference material or human
consultants when they get stuck. (06)
For that legacy re-engineering example, the computer ran
24/7 for 3 weeks to download and process all the files.
During that time, it logged all the problematical areas,
stated it best guess and confidence factors, and let the
two developers (Arun Majumdar and André LeClerc) decide
what to do about them. See slides (120 to 128) of (07)
http://www.jfsowa.com/talks/goal.pdf (08)
That project was done several years ago, and the equivalent
could be done much faster today. The computers can process
much larger files much more quickly, but they still need
human teachers and advisers to help them. (09)
John (010)
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