Hello John, (01)
On Sun, Aug 04, 2013 at 01:28:28PM -0400, John F Sowa wrote:
>That's fairly close to what the VivoMind software does. It does
>*not* have a stage that could be called "word sense disambiguation".
>Instead, the system uses a large inventory of graph patterns and
>a kind of associative memory for matching its inventory to the
>patterns that occur in the text. See the paradigm.pdf article.
[...]
>That's a good example. By using the original words from the text as
>labels on the graphs, the VivoMind software can use patterns from the
>literal use of the word 'look' to interpret metaphorical uses.
[...]
>That is what the VivoMind software would do with that sentence. It
>would use the common pattern for Eat to interpret it, but it would
>note that technologies are not a common kind of food. If it couldn't
>find a better pattern in its inventory, it would use the one it found.
>But it would also evaluate the pattern match as less than perfect. (02)
If I understand your mails and slides correctly, you parse natural language
to conceptual graphs using a link grammar parser. The words are the concept
nodes and the relation nodes ("roles") are chosen by the parser from the Verb
Semantics Ontology. The concept nodes (words) are usually not mapped to some
concept from an ontology (disambiguated). So they are usually only constrained
in their meaning by the text the system gets. (03)
The relations/roles connected to "look" and "eat" from the examples above are
determined by the parser. Any patterns using only those direct relations would
reuse knowledge that is alreay in the parser. I have some difficulty imagining
how bigger patterns could contribute to understanding or "interpreting" (as
you put it) the meaning of sentences using look and eat. (04)
This understanding seems to have areas of application (your customers) but
would you really compare it with human understanding of natural language ? (05)
Regards, (06)
Michael Brunnbauer (07)
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