Hi.  I don't understand the following statements.  Could you explain it some 
more?

- Natural language can be learned from examples. Formal language can not.

I think that you're basing this upon the methods that *you* would apply to each 
of the types of language.  It makes sense to me that because of the 
regularities of a formal language that you would be able to use more effective 
methods -- but it doesn't mean that the methods used on natural language 
wouldn't work (just that they would be as inefficient as they are on natural 
languages.

I would also argue that the same argument applies to the first statement of 
following the following two.

- Formal language must be parsed before it can be understood. Natural language 
must be understood before it can be parsed.


  ----- Original Message ----- 
  From: Matt Mahoney 
  To: agi@v2.listbox.com 
  Sent: Wednesday, October 22, 2008 9:23 PM
  Subject: Lojban (was Re: [agi] constructivist issues)


        Why would anyone use a simplified or formalized English (with regular 
grammar and no ambiguities) as a path to natural language understanding? Formal 
language processing has nothing to do with natural language processing other 
than sharing a common lexicon that make them appear superficially similar.

        - Natural language can be learned from examples. Formal language can 
not.
        - Formal language has an exact grammar and semantics. Natural language 
does not.
        - Formal language must be parsed before it can be understood. Natural 
language must be understood before it can be parsed.
        - Formal language is designed to be processed efficiently on a fast, 
reliable, sequential computer that neither makes nor tolerates errors, between 
systems that have identical, fixed language models. Natural language evolved to 
be processed efficiently by a slow, unreliable, massively parallel computer 
with enormous memory in a noisy environment between systems that have different 
but adaptive language models.

        So how does yet another formal language processing system help us 
understand natural language? This route has been a dead end for 50 years, in 
spite of the ability to always make some initial progress before getting stuck.

        -- Matt Mahoney, [EMAIL PROTECTED]

        --- On Wed, 10/22/08, Ben Goertzel <[EMAIL PROTECTED]> wrote:

          From: Ben Goertzel <[EMAIL PROTECTED]>
          Subject: Re: [agi] constructivist issues
          To: agi@v2.listbox.com
          Cc: [EMAIL PROTECTED]
          Date: Wednesday, October 22, 2008, 12:27 PM



          This is the standard Lojban dictionary

          http://jbovlaste.lojban.org/

          I am not so worried about word meanings, they can always be handled 
via reference to WordNet via usages like run_1, run_2, etc. ... or as you say 
by using rarer, less ambiguous words

          Prepositions are more worrisome, however, I suppose they can be 
handled in a similar way, e.g. by defining an ontology of preposition meanings 
like with_1, with_2, with_3, etc.

          In fact we had someone spend a couple months integrating existing 
resources into a preposition-meaning ontology like this a while back ... the 
so-called PrepositionWordNet ... or as it eventually came to be called the 
LARDict or LogicalArgumentRelationshipDictionary ...

          I think it would be feasible to tweak RelEx to recognize these sorts 
of subscripts, and in this way to recognize a highly controlled English that 
would be unproblematic to map semantically...

          We would then say e.g.

          I ate dinner with_2 my fork

          I live in_2 Maryland

          I have lived_6 for_3 41 years

          (where I suppress all _1's, so that e.g. ate means ate_1)

          Because, RelEx already happily parses the syntax of all simple 
sentences, so the only real hassle to deal with is disambiguation.   We could 
use similar hacking for reference resolution, temporal sequencing, etc.

          The terrorists_v1 robbed_v2 my house.   After that_v2, the jerks_v1 
urinated in_3 my yard.  

          I think this would be a relatively pain-free way to communicate with 
an AI that lacks the common sense to carry out disambiguation and reference 
resolution reliably.   Also, the log of communication would provide a nice 
training DB for it to use in studying disambiguation.

          -- Ben G



          On Wed, Oct 22, 2008 at 12:00 PM, Mark Waser <[EMAIL PROTECTED]> 
wrote:

            >> IMHO that is an almost hopeless approach, ambiguity is too 
integral to English or any natural language ... e.g preposition ambiguity

            Actually, I've been making pretty good progress.  You just always 
use big words and never use small words and/or you use a specific phrase as a 
"word".  Ambiguous prepositions just disambiguate to one of 
three/four/five/more possible unambiguous words/phrases.

            The problem is that most previous subsets (Simplified English, 
Basic English) actually *favored* the small tremendously over-used/ambiguous 
words (because you got so much more "bang for the buck" with them).

            Try only using big unambiguous words and see if you still have the 
same opinion.  

            >> If you want to take this sort of approach, you'd better start 
with Lojban instead....  Learning Lojban is a pain but far less pain than 
you'll have trying to make a disambiguated subset of English.

            My first reaction is . . . . Take a Lojban dictionary and see if 
you can come up with an unambiguous English word or very short phrase for each 
Lojban word.  If you can do it, my approach will work and will have the 
advantage that the output can be read by anyone (i.e. it's the equivalent of me 
having done it in Lojban and then added a Lojban -> English translation on the 
end) though the input is still *very* problematical (thus the need for a 
semantically-driven English->subset translator).  If you can't do it, then my 
approach won't work.

            Can you do it?  Why or why not?  If you can, do you still believe 
that my approach won't work?  Oh, wait . . . . a Lojban-to-English dictionary 
*does* attempt to come up with an unambiguous English word or very short phrase 
for each Lojban word.  :-)

            Actually, hmmmm . . . . a Lojban dictionary would probably help me 
focus my efforts a bit better and highlight things that I may have missed . . . 
. do you have a preferred dictionary or resource?  (Google has too many for me 
to do a decent perusal quickly)



              ----- Original Message ----- 
              From: Ben Goertzel 
              To: agi@v2.listbox.com 
              Sent: Wednesday, October 22, 2008 11:11 AM
              Subject: Re: [agi] constructivist issues







                Personally, rather than starting with NLP, I think that we're 
going to need to start with a formal language that is a disambiguated subset of 
English 


              IMHO that is an almost hopeless approach, ambiguity is too 
integral to English or any natural language ... e.g preposition ambiguity

              If you want to take this sort of approach, you'd better start 
with Lojban instead....  Learning Lojban is a pain but far less pain than 
you'll have trying to make a disambiguated subset of English.

              ben g 




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          -- 
          Ben Goertzel, PhD
          CEO, Novamente LLC and Biomind LLC
          Director of Research, SIAI
          [EMAIL PROTECTED]

          "A human being should be able to change a diaper, plan an invasion, 
butcher a hog, conn a ship, design a building, write a sonnet, balance 
accounts, build a wall, set a bone, comfort the dying, take orders, give 
orders, cooperate, act alone, solve equations, analyze a new problem, pitch 
manure, program a computer, cook a tasty meal, fight efficiently, die 
gallantly. Specialization is for insects."  -- Robert Heinlein




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