Artificial intelligence is maturing both as a powerful technology and
as a science. Expert systems are acknowledged as useful applications of
AI research; recent publications reflect upon AI methodology and
re-evaluate significant past efforts in the field. This thesis adds to
the growing body of evaluations and criticisms by describing MARVIN - a
"rational" reconstruction of key elements underlying three important
natural language analysis systems based on conceptual dependency theory.
In addition, MARVIN aims to control system complexity by separating
knowledge from control. An infrastructure based on PROLOG and an object
oriented representation scheme is instrumental in achieving this
A detailed description of MARVIN's knowledge base and control mechanism
is followed by an objective evaluation pointing out its many shortcomings.
In addition, MARVIN is compared with ELI - one of the conceptual
dependency systems which it aims to mimic. MARVIN fails to achieve the
flexibility and power demonstrated by ELI, but it redeems itself on
design. The thesis concludes on a positive note with respect to conceptual
dependency theory: it is a viable alternative to traditional parsing
techniques; but needs to be strengthened considerably to encompass a
broader linguistic scope, and to substantiate its psychological claims.
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