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.

natural language processing, semantic memory, text
comprehension, question answering, artificial 
intelligence, human memory simulation

3.36 3.42 3.62 3.65 3.74

CA720107 JB February 1, 1978  9:20 AM

1400	4	2396
1553	4	2396
1945	4	2396
2127	4	2396
2127	4	2396
2178	4	2396
2309	4	2396
2310	4	2396
2396	4	2396
2396	4	2396
2396	4	2396
2396	4	2396
2561	4	2396
2730	4	2396
1487	5	2396
1856	5	2396
2092	5	2396
2127	5	2396
2396	5	2396
2396	5	2396
2396	5	2396