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