The current best-way is to just dump the link-grammar parses into the
atomspace.  There's an atom for that; you just invoke it and the parses
magically appear in the atomspace.

The link-grammar parses are fairly low-level, but accurate. higher-level is
relex, but it is less accurate, its fuzzier. You need to run the relex
server for that. You pipe it a sentence, and the atoms just show up in the
atomspace.

Higher-up is relex2logic aka R2L which attempts to create logical forms for
English sentences. Its got issues; the biggest issue is that the PLN solver
doesn't work very well on the output that R2L is generating.

There's nothing wrong with starting with the low-level parses, and trying
to do something with those.  The higher-level layers, although more
abstract, are .. messy.

--linas



On Wed, Dec 20, 2017 at 10:53 PM, Mindey I. <[email protected]> wrote:

> Hello Everyone,
>
>
> say I'd like to convert natural language specification of goals into
> atomese, so as to be able to apply the opencog's solvers to ground them,
> and possibly extend opencog with other types of solvers.
>
> For something specific, let's say we have descriptions of UN sustainable
> development goals ( http://www.un.org/sustainabledevelopment/
> sustainable-development-goals/ , https://en.wikipedia.org/wiki/
> Sustainable_Development_Goals ), which are phrased in human language.
>
> And, let's say we want to convert them into atomese, in such a way, as to
> precisely capture their meaning.
>
> In other words, we have some texts, rather than spoken language. What
> would be the current best way of converting plain text in a natural human
> language to a semantic representation in atomese, assuming global/general
> context?
>
>
> Thanks
> Mindey
>
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*"The problem is not that artificial intelligence will get too smart and
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