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 agi | Archives | Modify Your Subscription agi | Archives | Modify Your Subscription -- 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. 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