[Corpora-List] algo for semantic structure

[Dan Garrette]

Linas,

I guess my response should have been characterized as "beginner" instead of
a "common".  ;)

Thanks for filling in with the more advanced techniques!

-Dan


On Mon, Sep 8, 2008 at 1:30 PM, Linas Vepstas <linasvepstas at gmail.com>wrote:

> 2008/9/7 Dan Garrette <dhgarrette at gmail.com>:
> > Vrone,
> >
> > The most common way to turn syntax into semantics is by defining logical
> > expressions for each part of speech in a sentence and then composing
> those
> > parts to make the meaning of the whole.  For instance, to find the
> meaning
> > of the sentence "John sees a man" we can start by assigning a logical
> > meaning of each word in the sentence:
>
> Surely this is the "least common" way of doing things, as
> it completely ignores both traditional work on parsing, as
> well as being uninformed by any sort of corpus statistics.
> One usually finishes by assigning meaning, not starts.
>
> The most "common" way might be to employ a number
> nested, layered techniques, from part of speech taggers
> and morphology stemmers, to various phrase structure
> or dependency grammars to obtain relations, for example
>
>  subj(John, sees)    # who is seeing?
>  obj (a man, sees)  # what are they seeing?
>
> Meaning is provided by both the structure of the sentence,
> plus prior knowledge that the word "John" might
> be a man, or might be a toilet, and "see" might be "view"
> or it might be "accept visitors", so that "John sees a man"
> might be a funny way of saying "the toilet is accepting
> visitors".
>
> Importantly, one can make rather good progress
> by abandoning syntactic structure completely; see
> for example Radu Mihalcea's work on word-sense
> disambiguation, which, in a nutshell, solves a Markov
> chain on word senses.  There's not a drop of grammar
> or parsing in it (or first-order logic either).  Its a solid
> result which should cause anyone working on
> this-n-such theory of grammar to stop, pull their head
> out of the sand, and re-evaluate their strategy.
>
> The work described in  http://nltk.org/doc/en/ch11.html
> is curious, but I'd think a much stronger approach would be
> to assign probabilities to each statement of first-order
> logic (and thus obtain, for example, a "Markov logic
> network", or a generalization, the PLN) Such probabilities
> would be computed from corpus analysis.
>
> I agree that "sense" can be considered to be the set of
> predicates and rules that were triggered during a parse.
> But the examples at that url also seems to make use of
> somewhat olde-fashioned ideas like NP and VP, when
> there in fact seem to be a much much richer and broader
> set of relationships between words, phrases, colligations, etc.
> e.g. the hundreds of dependency links and thousands of
> rules in link-grammar, to the thousands of framenet-style
> frames.  I just don't see that a first-order logic will ever
> successfully capture this -- I'd say that Cyc illustrates what
> the limit of that technique is.
>
> --linas
>
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