I go with it more as the semantics are general mathematical operators that 
instantiate their operational relationships situationally. Example, the AGI is 
observing a local entity the situational sematic operators resolve and coalesce 
inductively and deductively with a reflective complexity generated from a 
decided temporal expense. Other local sematic operators are resolved from other 
situations. Correlations and reuse of these operators across situations result 
in efficiencies which are aligned with learning and form a natural language 
semantic. The syntactics form from dimensionally projecting the semantics into 
an environment of particular Communication Complexity (Yao) characteristic. But 
the precise algebraic structure of the all those operators, both local and 
global, and glocal, comes up as, some sort of network... an octonionic 
structured network maybe? :)

 

John 

 

From: YKY (Yan King Yin, 甄景贤) via AGI [mailto:[email protected]] 
Sent: Thursday, June 19, 2014 4:57 PM
To: AGI
Subject: Re: [agi] Re: [GI] A question about tensor product

 

On Thu, Jun 19, 2014 at 3:44 AM, Juan Carlos Kuri Pinto via AGI 
<[email protected] <mailto:[email protected]> > wrote:

I would not use any mathematical tool unless it perfectly fits the problem to 
solve. I doubt tensor products can solve NLP. But I can be wrong. I would 
rather use analogical correspondences via Markov models for NLP. But I can be 
wrong too. :)

 

 

​If you model things as Markov chains of probabilities then you're really doing 
NLP​ and purely syntactically.  But I think a more effective approach is to 
ignore the whims and irregularities of natural language and model "pure 
semantics" instead.  That is the essence of a logic engine.

 

We just need to find out the algebraic form of the semantics.  The vectors / 
tensors can be seen as representations of the algebraic structure.

 

 




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