Hello Linas. If you leave it to the learning mechanism, aren't you inevitably
going to get crossed links? To take an even simpler example, "It was raining",
your learning mechanism should work out three predictions:
* that "was" needs a subject (i.e. a preceding noun or pronoun).
* that any form of the verb RAIN needs the pronoun "it" as its subject (as
in "It rained").
* that "was" needs (or at least accepts) an ing-form verb after it.
When you put these expectations together, you find a dependency triangle, with
subject links from both verbs to "it" and dependency from "was" to "raining".
Since both of the "it" links are the same ('subject'), there's no reason for
assigning them to different levels of structure (deep vs surface), so you get a
topological tangle.
Dick
On 16/11/2018 22:05, Linas Vepstas wrote:
I hit "send" too soon, without finishing the thought:
On Fri, Nov 16, 2018 at 3:02 PM Linas Vepstas
<[email protected]<mailto:[email protected]>> wrote:
For example, this parse makes sense, and seems right:
+-------->WV------->+
+---->Wd-----+ |
| +Ds**c+-Ss*s-+---Pa--+
| | | | |
LEFT-WALL the dog.n was.v-d black.a
but there is another possibility, that kind-of makes sense (and perhaps
language learning will find):
+---->Wd---->+
| +-->adjcomp--->+
| +Ds**c+ +<-cop<-+
| | | | |
LEFT-WALL the dog.n was black
Here, adjcomp is "adjectival compliment" and "cop" was copula. Some dependency
grammars draw this graph. Some call it "predicative adjectival modifier". Lets
quibble. Note that I did not draw an arrow from subject to verb. I could, I
suppose. Note that it is now IMPOSSIBLE to draw an arrow from root/left-wall
to the verb, because it would require a
link-crossing, it would have to cross over the adjcomp arrow.
Thus, if you want to draw an arrow from root to head-verb, and also get a
planar graph, you are not allowed to draw the adjcomp/predadj arrow. That
helps explain what LG does.
It also helps make clear that the no-links-crossing constraint is imperfect. It
seems reasonable, but clearly, there is a violation in the above rather
trivial sentence!
OK, to finish this thought. Let us speculate what an MST parse of this sentence
might be like. It depends on the MI values for the word-pairs MI(dog,was)
MI(was,black) and MI(dog,black) I don't know what these are, but clearly they
will be different for a corpus of kids-lit, than a corpus of math texts.
Next question: what happens when words are sorted into categories? What is
MI(dog, some color)? What is MI(some animal, some color)? What is MI(physical
object, some color)?
I don't have a good story here, except to say that copulas and predicative
adjectives prsent maybe the simplest-possible example of a difficulty of moving
from surface syntax (SSynt, what LG does) to deep syntax (DSynt, what MMT
does). Yet, this move is a critical one.
I'm currently thinking of it as a graph-write rule, that converts the SSynt
graph into a PLN graph
EvaluationLink
PredicateNode "has color"
ListLink
Concept "dog"
Concept "black"
Or, perhaps as Nil might like to write:
LambdaLink
VariableList
Variable $PHY
Variable $COL
AndLink
EvaluationLink
PredicateNode "has color"
ListLink
Variable $PHY
Variable $COL
InheritanceLink
Variable $PHY
Concept "physical object"
InheritanceLink
Variable $COL
Concept "color"
Of course, even the above representation is wrong, in several ways, but
nit-picking it at this stage is counter-productive.
The question is: given a learned grammar, with statistics, how to we get to the
DSynt or the opencog variant? Well, the now-quite-old Dekang Lin DIRT paper,
and the newer-but-still-old Poon&Domingos unsupervised learning paper show the
way.
Onward ho!
Linas
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