Subject: [ontolog-forum] Processing NLs and controlled NLs
From: John F Sowa <[email-masked]>
To: ontolog-forum <[email-masked]>, "[email-masked]" <[email-masked]>
Date: Tue, 26 Jan 2016 15:04:49 -0500
List-Id: <ontolog-forum.googlegroups.com>

Following is a slightly edited version of a note I sent to the Ontology-
Summit email list.  I'm sending it to these lists because it makes a
useful distinction about methods for processing NLs and CNLs.

John

-------- Forwarded Message --------

On 1/26/2016 11:16 AM, CP wrote:
> If you are trying to construct something that will end up in a computer,
> then at some stage in the process it is likely you will have to produce
> something formal (well with current technology, you have little or no
> choice).  And, NLs are not formal (yet?).

That is certainly true.

> Therefore ... well you can work out the rest.

Yes:

  1. Natural languages are the most general and expressive notations
     for everything that anyone might want to say about anything.
     Even the most sophisticated scientists, mathematicians, and
     logicians use NLs to explain and discuss their formalisms
     with their colleagues.

  2. Every formal notation of any kind is an *abstraction from* some
     NL that imposes some restrictions on what can said in order to
     accommodate the limitations of current AI technology.

  3. Any statement in any formalism that cannot be translated
     to any NL is guaranteed to be misunderstood and misapplied
     by any human who writes it, reads it, or uses it in any way.
     That includes the humans who designed the notation.

  4. Therefore, any semi-automated system or methodology for developing
     computer applications must be able to relate NL specifications to
     the formalism and to relate the formalism to NL explanations.

'Semi-automated' means that some human analyst or developer is involved
in doing or assisting the mappings to and from the NLs.  In general,
a fully automated mapping from a formal notation to a controlled subset
of some NL can be done with current technology.

Automated mappings from unrestricted NLs to any formalism are much more
difficult.  But a great deal can be done with semi-automated methods.

For more detail, see the slides on "Natural Language Understanding",
which I presented at the Data Analytics Summit in December:
http://www.jfsowa.com/talks/nlu.pdf

For an example of information extraction from texts, see slides 41 & 42.

Slides 43 to 51 show how the automated part of the NL interpretation
depended very heavily on prior background knowledge.  Slide 45 shows
an example of the unrestricted NL (with no human annotations).  The
analysis of that text depended on a *prior* extraction of background
knowledge from programs written in a formal language (COBOL).

That example was ideally suited to the VivoMind/Kyndi technology.
It's rare that you can find applications where the formally derived
knowledge so precisely overlaps the NL documents.

See slides 52 to 59, where a partial overlap (textbook chapters and
research reports) enable NL info from the textbook to be used to
analyze the research reports.  Since both the textbook and the
reports were both written in English, the analysis did not reach the
same depth of detail as in slides 43 to 51.  But it was much better
than an attempt (with VivoMind software) to analyze the reports
without using the textbook as a knowledge source.

These are research projects that used real data, not toy examples.
For the legacy re-engineering project (slides 43 to 51), the semi-
automated work required 15 person-weeks of human effort.  The client
said that the results it produced were "exactly what they wanted".
But a major consulting firm had estimated that it would require 80
person-years (40 people for 2 years) for that project.

John

And by the way, Slide 2 of nlu.pdf has a pointer to videos of talks
presented at the Data Analytics Summit.  They illustrate a variety
of ways of processing documents and extracting various kinds of
information from them.

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