----- Original Message ---- From: Benjamin Goertzel <[EMAIL PROTECTED]> To: agi@v2.listbox.com Sent: Wednesday, January 9, 2008 4:04:58 PM Subject: Re: [agi] Incremental Fluid Construction Grammar released
> And how would a young child or foreigner interpret on the Washington > Monument or "shit list"? Both are physical objects and a book *could* be > resting on them. Sorry, my shit list is purely mental in nature ;-) ... at the moment, I maintain a task list but not a shit list... maybe I need to get better organized!!! > Ben, your question is *very* disingenuous. Who, **me** ??? >There is a tremendous amount of > domain/real-world knowledge that is absolutely required to parse your > sentences. Do you have any better way of approaching the problem? > > I've been putting a lot of thought and work into trying to build and > maintain precedence of knowledge structures with respect to disambiguating > (and overriding incorrect) parsing . . . . and don't believe that it's going > to be possible without a severe amount of knwledge . . . . > > What do you think? OK... Let's assume one is working within the scope of an AI system that includes an NLP parser, a logical knowledge representation system, and needs some intelligent way to map the output of the latter into the former. Then, in this context, there are three approaches, which may be tried alone or in combination: 1) Hand-code rules to map the output of the parser into a much less ambiguous logical format 2) Use statistical learning across a huge corpus of text to somehow infer these rules [I did not ever flesh out this approach as it seemed implausible, but I have to recognize its theoretical possibility] 3) Use **embodied** learning, so that the system can statistically infer the rules from the combination of parse-trees with logical relationships that it observes to describe situations it sees [This is the best approach in principle, but may require years and years of embodied interaction for a system to learn.] Obviously, Cycorp has taken Approach 1, with only modest success. But I think part of the reason they have not been more successful is a combination of a bad choice of parser with a bad choice of knowledge representation. They use a phrase structure grammar parser and predicate logic, whereas I believe if one uses a dependency grammar parser and term logic, the process becomes a lot easier. So far as I can tell, in texai you are replicating Cyc's choices in this regard (phrase structure grammar + predicate logic). Yes, the Texai implementation of Incremental Fluid Construction Grammar follows the phrase structure approach in which leaf lexical constituents are grouped into a structure (i.e. construction) hierarchy. Yet, because it is incremental and thus cognitively plausible, it should scale to longer sentences better than any non-incremental alternative. The mapping of form to predicate logic (RDF-style) is facilitated both by Fluid Construction Grammar (FCG) and by Double R Grammar (DRG). I am using the production rule engine from FCG, enhanced to operate incrementally, and the construction theory from DRG whose focus is on referents and the relationships among them. For quantifier scoping I expect to use Minimal Recursion Semantics which should plug into the FCG feature structure. -Steve Stephen L. Reed Artificial Intelligence Researcher http://texai.org/blog http://texai.org 3008 Oak Crest Ave. Austin, Texas, USA 78704 512.791.7860 ____________________________________________________________________________________ Never miss a thing. Make Yahoo your home page. http://www.yahoo.com/r/hs ----- This list is sponsored by AGIRI: http://www.agiri.org/email To unsubscribe or change your options, please go to: http://v2.listbox.com/member/?member_id=8660244&id_secret=84215091-e9ef0b
