> 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). In Novamente, we are aiming at a combination of the 3 approaches. We are encoding a bunch of rules, but we don't ever expect to get anywhere near complete coverage with them, and we have mechanisms (some designed, some already in place) that can generalize the rule base to learn new, probabilistic rules, based on statistical corpus analysis and based on embodied experience. In our rule encoding approach, we will need about 5000 mapping rules to map syntactic parses of commonsense sentences into term logic relationships. Our inference engine will then generalize these into hundreds of thousands or millions of specialized rules. This is current work, research in progress. We have about 1000 rules in place now and will soon stop coding them and start experimenting with using inference to generalize and apply them. If this goes well, then we'll put in the work to encode the rest of the rules (which is not very fun work, as you might imagine). Emotionally and philosophically, I am more drawn to approach 3 (embodied learning), but pragmatically, I have reluctantly concluded that the hybrid approach we're currently taking has the greatest odds of rapid success. In the longer term, we intend to throw out the standalone grammar parser we're using and have syntax parsing done via our core AI processing -- but we're now using a standalone grammar parser as a sort of "scaffolding." I note that this is not the main NM R&D thrust right now -- it is at the moment somewhat separate from our work on embodied imitative/reinforcement/corrective learning of virtual agents. However, the two streams of work are intended to come together, as I've outlined in my paper for WCCI 2008, http://www.goertzel.org/new_research/WCCI_AGI.pdf -- Ben -- Ben G ----- 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=83967477-a9e1c4
