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.
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