This work is a progress along a clearly stated line of research. It is about ways of managing ambiguity. The dissertation is not about a complete AGI system, it does not go into machine learning. It is not about discovering meaning, but analysing meaning: the interpretation of output is fully specified by a concrete application of the system (I would call this a symbolic approach).
On 5/8/07, Matt Mahoney <[EMAIL PROTECTED]> wrote:
--- Lukasz Stafiniak <[EMAIL PROTECTED]> wrote: > Iddo Lev has a more practical answer: > http://www.stanford.edu/~iddolev/pulc/current_work.html Just looking at it briefly, it appears to clearly present the many problems with natural language understanding (i.e. various forms of ambiguity). Then it addresses these problems with a huge, complicated set of language rules that have to be hand coded. Am I correct? If so, this approach is not really new. I would be interested in models that can learn language rules from unlabeled text.
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