MUSE: A Model To Understand Simple English
MUSE is a computer model for natural language
processing, based on a semantic memory network 
like that of Quillian's TLC.  MUSE, from a Model to Understand
Simple English, processes English sentences 
of unrestricted content but somewhat restricted format.
 The model first applies syntactic analysis to 
eliminate some interpretations and then employs a simplified
semantic intersection procedure to find 
a valid interpretation of the input.  While the semantic
processing is similar to TLC's, the syntactic 
component includes the early use of parse trees and special
purpose rules.  The "relational triple" notation 
used during interpretation of input is compatible with MUSE's
memory structures, allowing direct verification 
of familiar concepts and the addition of new ones. 
MUSE also has a repertoire of actions, which range 
from editing and reporting the contents of its own
memory to an indirect form of question answering. 
 Examples are presented to demonstrate how the model interprets
text, resolves ambiguities, adds information 
to memory, generalizes from examples and performs various actions.
CACM January, 1972
McCalla, G. I.
Sampson, J. R.
