On the SourceForge project site, I just released the Java library for
Incremental Fluid Construction Grammar.
Fluid Construction Grammar is a natural language parsing and generation system
developed by researchers at emergent-languages.org. The system features a
production rule mechanism for
One of the things that I quickly discovered when first working on my
convert it all to Basic English project is that the simplest words
(prepositions and the simplest verbs in particular) are the biggest problem
because they have so many different (though obscurely related) meanings (not
to
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
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.
How would your rules handle the on
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)
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
Processing a dictionary in a useful way
requires quite sophisticated language understanding ability, though.
Once you can do that, the hard part of the problem is already
solved ;-)
Ben
On Jan 9, 2008 7:22 PM, William Pearson [EMAIL PROTECTED] wrote:
On 09/01/2008, Benjamin Goertzel [EMAIL
