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A neuro‐propositional model of language processing
Author(s) -
Buchheit Paul
Publication year - 1999
Publication title -
international journal of intelligent systems
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.291
H-Index - 87
eISSN - 1098-111X
pISSN - 0884-8173
DOI - 10.1002/(sici)1098-111x(199906)14:6<585::aid-int3>3.0.co;2-5
Subject(s) - computer science , backward chaining , reification (marxism) , propositional calculus , sentence , natural language processing , knowledge base , descriptive knowledge , artificial intelligence , propositional formula , connectionism , propositional variable , inference , programming language , inference engine , artificial neural network , description logic , knowledge management , politics , political science , intermediate logic , law
An implemented model of language processing has been developed that views the propositional components of a sentence as neural units. The propositional sentence units are linked through symbolic, reified representations of subordinate sentence parts. Large numbers of these highly standardized propositional units are encoded in a manner that interconnects propositional data through the declarative knowledge base structures, thus minimizing the importance of the procedural component and the need for backward chaining and inference generation. The introduction of new sentence information triggers a connectionist‐like flurry of activity in which constantly changing propositional weights and reification strengths effect changes in the belief states encoded within the knowledge base. ©1999 John Wiley & Sons, Inc.