tech report on story comprehension

[Mark St. John]

		The Story Gestalt
Text Comprehension by Cue-based Constraint Satisfaction

		Mark F. St. John
	Department of Cognitive Science, UCSD

		   Abstract:
Cue-based constraint satisfaction is an appropriate algorithm for many
aspects of story comprehension.  Under this view, the text is seen to contain
cues that are used as evidence to constrain a full interpretation of a story.
Each cue can constrain the interpretation in a number of ways, and cues are
easily combined to produce an interpretation.  Using this algorithm, a number
of comprehension tasks become natural and easy.  Inferences are drawn and
pronouns are resolved automatically as an inherent part of processing the
text.  The developing interpretation of a story is revised as new information
becomes available.  Knowledge learned in one context can be shared in new
contexts.  Cue-based constraint satisfaction is naturally implemented in a
recurrent connectionist network where the weights encode the constraints.
Propositions are processed sequentially to add constraints to refine the
story interpretation.  Each of the processes mentioned above is seen as an
instance of a general constraint satisfaction process.  The model learns its
representation of stories in a hidden unit layer called the Story Gestalt.
Learning is driven by asking the model questions about a story during
processing.  Errors in question answering are used to modify the weights in
the network via Back Propagation.

                                                                             
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The report can be obtained from the neuroprose database by the
following procedure.

unix> ftp cheops.cis.ohio-state.edu          # (or ftp 128.146.8.62)
Name (cheops.cis.ohio-state.edu:): anonymous
Password (cheops.cis.ohio-state.edu:anonymous): neuron
ftp> cd pub/neuroprose
ftp> type binary
ftp> get
(remote-file) stjohn.story.ps.Z
(local-file) foo.ps.Z
ftp> quit
unix> uncompress foo.ps.Z
unix> lpr -P(your_local_postscript_printer) foo.ps