Re: [ontolog-forum] Semantics, Representations and Grammars for Deep Lea

[John Bottoms <john@xxxxxxxxxxxxxxxxxxxx>]

I'm Skeptical,

A total of 103 references for a 16 page paper seems a bit out of whack. And his approach relies on inputs to the NN's that, "A biologically-plausible deep learning algorithm should take advantage of the particularities of the reinforcement learning setting." That amounts to saying that an appropriately sized corpus is required.

Further, it is always a red flag for me is when someone coins a new term or acronym such as "KICKS", when there are better ways to explain the algorithm. I believe he presents a reasonable overview of some useful techniques that would be better conceived using semantics.

Semantics can help. The reason linguistics provides valuable tools is because natural languages have undergone thousands of years of adaptation and pruning that embody the deep learning of the members of the community of interest. We should take advantage of that learning in a semantic fashion, rather than trying to recreate those lessons. Humans can provide the right answers if we carefully ask the correct questions.

-John Bottoms
 Concord, MA USA

On 10/25/2015 3:32 PM, Rich Cooper wrote:

Dear OntoLogicists,

 

The subject says it all.  This is a 17 page paper with 3 pages more in references about how the neural net crowd seems to have straightened a curve in getting to natural language in a deeper sense than just voice recognition.  The paper is at:

 

http://arxiv.org/pdf/1509.08627.pdf

 

The author seems to have the EE math culture viewpoint leading to some very interesting ways to learn such odd things as those discussed in Women, Fire and Dangerous Things by exposure to a large enough number of samples. 

 

Does anyone have a good tutorial in mind on recent NN to NLP practice?

 

Sincerely,

Rich Cooper,

Rich Cooper,

 

Chief Technology Officer,

MetaSemantics Corporation

MetaSemantics AT EnglishLogicKernel DOT com

( 9 4 9 ) 5 2 5-5 7 1 2

http://www.EnglishLogicKernel.com

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