A perhaps nicer example is Get me the ball
for which RelEx outputs definite(ball) singular(ball) imperative(get) singular(me) definite(me) _obj(get, me) _obj2(get, ball) and RelExToFrame outputs Bringing:Theme(get,me) Bringing:Beneficiary(get,me) Bringing:Theme(get,ball) Bringing:Agent(get,you) Note that the RelEx output is already abstracted and "semantified" compared to what comes out of a grammar parser. -- Ben On Jan 9, 2008 5:59 PM, Benjamin Goertzel <[EMAIL PROTECTED]> wrote: > > > > Can you give about ten examples of rules? (That would answer a lot of my > > questions above) > > That would just lead to really long list of questions that I don't have time > to > answer right now.... > > In a month or two, we'll write a paper on the rule-encoding approach we're > using, and I'll post it to the list, which will make this approach clearer. > > > Where did you get the rules? Did you hand-code them or get them from > > somewhere? > > As you know we have a system called RelEx that transforms the output of > the link parser into higher-level semantic relationships. > > We then have a system of rules that map RelEx output into a set of > frame-element relationships constructed mostly based on FrameNet. > > For the sentence > > Ben kills chickens > > RelEx outputs > > _obj(kill, chicken) > present(kill) > plural(chicken) > uncountable(Ben) > _subj(kill, Ben) > > and the RelExToFrame rules output > > Killing:Killer(kill,Ben) > Killing:Victim(kill,chicken) > Temporal_colocation:Event(present,kill) > > But I really don't have time to explain all the syntax and notation in > detail... if it's not transparent... > > And I want to stress that I consider this kind of system pretty > useless on its own, it's only potentially valuable if coupled with > other components like we have in Novamente, such as an uncertain > inference engine and an embodied learning system... > > Such rules IMO are mainly valuable to give a starting-point to a > learning system, not as the sole or primary cognitive material of an > AI system. And using them as a starting-point requires very careful > design... > > The 5000 rules figure is roughly rooted in the 825 frames in FrameNet; > each frame corresponds to a number of rules, most of which are related > to specific verb/preposition combinations. > > Another way to look at it is that each rule corresponds roughly to a > Lojban word/argument combination... pretty much, FrameNet and the > Lojban dictionary are doing the same thing, which is to precisely > specify commonsense subcategorization frames. > > -- Ben > ----- 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=83993607-803936
