Hi,

> Just got out of surgery for my broken leg; this email attempts to prove that
> the general anesthesia didn't kill too many brain cells.

Oh shit... so that little ankle-twist you got in Lalibela was actually
a broken bone??!!  ;o  that would explain why it was taking so f**king
long to heal ... ;/

> Its a report on
> some the language-learning results.
...
> (mst-parse-text "dogs eat dog food")
> ((7.329 ((2 (WordNode "eat" (ctv 20938))) (4 (WordNode "food" (ctv
> 73924)))))
> (7.047 ((3 (WordNode "dog" (ctv 41896))) (4 (WordNode "food" (ctv 73924)))))
> (5.050 ((1 (WordNode "dogs" (ctv 14852))) (2 (WordNode "eat" (ctv 20938))
> ))))
...
> (mst-parse-text "It is not uncommon to discover strange things")
>
> ((7.515 ( (WordNode "not" ) (WordNode "uncommon" )))
>  (4.142 ( (WordNode "is" ) (WordNode "uncommon" )))
>  (4.412 ( (WordNode "It" ) (WordNode "is" )))
>  (2.739 ( (WordNode "uncommon" ) (WordNode "to" )))
> (3.529 ( (WordNode "to" ) (WordNode "discover" )))
> (0.822 ( (WordNode "to" ) (WordNode "things" )))
> (6.171 ( (WordNode "strange" ) (WordNode "things" ))))
>
> Almost right -- the stinker in there is "to things" and it has a terrible
> MI.  The correct link would have been "discover things" but this word-pair
> was never ever observed.

Yeah, cool!  We are definitely getting somewhere.  These results are
all with an overly small corpus (as well as being the first stage of
the iterative process) so some dopey mistakes are to be expected...

***
Anyway, clustering that reveals that cats, dogs, etc are similar
should help with this, or so goes the hypothesis.
***

Yes.   Ruiting is ready to start playing with clustering approaches,
as soon as you give her an Atomspace of mst-parsed sentences that you
think is a passable corpus for clustering experimentation...

-- Ben

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