[ranch vermin]

Korrelan, thats a tokenizer as I read it.  It works on repetivities to develop a taxonomy.


Spydaz->
"his girlfriend Pats a dog" would make sense "His girlfriend Pat's a dog. "    - that could be solved by looking back past the sentence, in a machine.


Im fully back into this 'simulation of reality is a type of intelligence' idea.  a markov chain being a simple 1d example of it.

What are cats?
 Cats are ......   *what they do*

i like to say everything is just one thing.
so i like to say a cat has hair.     so the cat does it.    so then you decide how many other things also do it, and then you made the most of 'cat has hair' 


[EDIT][ I just deleted the last note because it was too long. [/EDIT]

GENERALIZING NLP->
the more words you say are the same in some context and you are RIGHT,  the more things you know, that you didnt see completely sensitively.

[spydaz]

In the logic stages :


All cat have fur ....
Some cats have fur
..... Age old argument

Inference

[8pla.net]

Thanks for the criticism... 

That is something to think about.   Here is what I was thinking originally...

"His girlfriend Pat's a dog. " equals "His girlfriend Pat is a dog. " (without the contraction).

But, I did just made it up, so I admit to not having given it too much thought.

And, by the way, no offense to any girlfriend named Pat!   

Pat was selected because it is both a name and a verb.

[spydaz]

The Markov chain could be used to correct such mistakes by predicting what should have been said ....

That error could occur in speech recognition situations where punctuation is not recognised without specifying ....
Such a correction matrix would be necessary.  Specifically to add missing punctuational which should have been detected by an extra long or short pause like a "rest" or "semi breath" as In musical notation. This is also how it would detect if it's pat the verb or pat the pronoun ... Similar to the spell check in word ... Which uses some grammatical rules.
By adding lessons learnt statistically and probabilistically by data mining regression and decision trees ... And maybe cluster analysis K-nearest neighbours  ... I tend not to implement neural networks in data mining as its like cheating the data mining algorithm after knowledge travels through multiple neutrons the neural  network will converge therefore become "overfit" given enough time all neural networks will converge .... But that's another debate plus a bit complicated for here ...

Data mining produces probabilistic results based on past history .. Yet if the history does not contain every possibility then it's impossible for it to be useful as there will always been something uncalculated there fore the answer produced is not correct ... Another reason why parts of speech tagging based on data mining techniques cannot produce 100percent correctness ...
This rough guess of pat as a verb or as a noun would need naive bayes probability to guess based on the last known usage of pat in the thought or conversation . guessing is never good yet it can produce some "fuzzy" results hence fuzzy logic ... Lol... In precise ....

Data mining / machine learning is good when you have all possibilities and combinations already else it's just guessing which is the same as gambling .. Just reducing the odds

[ivan.moony]

I've been searching for BNF grammar rules for English, but I couldn't find any on the web. I guess one need addition of predicates to BNF to describe languages such is English. I wonder if predicate extension is enough.

[8pla.net]

I enjoyed reading your valuable approach, this morning over coffee.  When you said, "But that's another debate plus a bit complicated for here ..." I thought, "Well... I like complicated."   We learn the most from our mistakes, (paraphrasing a science documentary I watched recently on prehistoric man). 

My approach  concerns  practical A.I. engine design, how original coding may handle some of these issues. An A.I. engine knows beforehand that it will be guessing, because it has exhausted all other options.  For efficiency, the matching algorithm may be hard coded, sorted in reverse order by the amount of which the A.I. is guessing.  So the stimulus may set flags as it falls through several conditional branches, with a complete guess being a catch-all at the bottom.

Using intermediate coding techniques, I proffer an A.I. engine may predict, by simply crawling through its own knowledgebase, when and where a mistake is probable, to simulate confidence in its response.

[spydaz]

I think a combination. Of approaches , Markov chains , statistics produced from large correct data sets would produce the desired result ...

The original datasets used to produce the results would not be added to the ai .. Just the predictive rules defined by the data mining tasks ...   

Differently , by creating a datacube you can produce massively complex queries which based on the knowledge base created by collecting , predicates , nouns , subjects articles ... Etc ... All information stored about a cat in all its diffent collection methods can be retrieved ... Knowledge storage is also an art form In its self .... Fact tables or relational ...
Naturally relational , yet this has many query problems as well as insert problems ...

Knowledge storage and Nlp have both issues to face ...

[Snowman]

Well, here's another video.... perhaps a bit simple for some, but like ranch implied, this video is made just for the fun of it. 

[ranch vermin]

good to see you again snowman.

Im right with you on this,  ive got very similar thinking.

I think its all like one of those choose your own adventure books.

its a bit of a moonshot isnt it,   as far as *my* working implementation theory goes,  the chain keeps the users text in it, the nlp is saying what pathway points it can cross logically to increase store, (says what words it can interchange) and then its got a memory that remembers everything that didnt work, out of random trials.   and it seems to be what it likes to do is avoid swear words from the user.

simple,    but you might know that ive been working on a raw data eye as well so it can see and ive been working on 3d motor as well.  for the whole shabammer.

but its a moon shot for sure,  and the only bit i dont totally know is how the nlp is going to find these interchangable words, the mytonymy,  which then gives it more ways to order whats inside it, which is the same as knowing more.

[ranch vermin]

some stuff in here on automatic synonym generation and some noun taxonomy stuff->

https://www.hucompute.org/data/pdf/vdbrueck/knowaqu_en.pdf

which is what i just worked out is N.L.P...  or rather GENERALIZATION DETECTION.  or REDUCTION ALGORYTHM.

[spydaz]

http://linguistics.online.uni-marburg.de/

This is where you can learn all about language processing ....

Free university ...

I've done all the modules ...
Take a look ... It's better than reading a paper written by students ...