Le sam. 1 déc. 2018 à 17:00, Matías Guzmán Naranjo <[email protected]> a
écrit :

> Hi Amirouche,
>
> yes, those are the feature structures I am talking about. Your idea for
> representing them seems interesting, I am wondering whether you have a
> solution for unification?
>

Maybe the term unification is misleading here. That's why I asked the
question initially. I also asked the question because I am interested in
NLP and feature structures seems to be thing in this field. But I failed to
find more ressources to better understand the use cases.

Quick answer is that it doesn't do unification in terms of feature
structures. It does unification of triples with a semantic that *looks*
like SPARQL (without support for OPTIONAL).

Say I have [Agr [number sg]] and want to unify it with [Agr [person 1]], it
> should produce: [Agre [number sg, person 1]] (where order doesn't matter).
>
However, it should fail if I want to unify [Agr [number sg]] and [Agr
> [number pl]].
>

But it could unify with [Agr [number sg, gender f]]?


> Ideally, it should also run in reverse: I should be able to ask what [Agr
> [number sg]] needs to unify with to produce [Agr [number sg, person 1]].
>

That is an interesting problem.


> Can your system do this?
>

My system narrow the search space. So indeed, if you want to know
everything that can unify with [Agr [number sg]] you *could* query every
every facts that are an Agr (instead of all the facts) and then filter
manualy those that don't have [number sg] or have something else and do
that recursively... In theory it's possible to do such thing I guess.

Like I said previously, it can not do feature-structure unification as-is.
To support that last query it requires a minikanren with constraints
support (and possibly to support bigger than RAM dataset it would require
streamable solutions).

I will come back to this subject at some point, but the lack of real-world
problem to solve doesn't make it appealing.

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