Symbolic connectionism in natural language disambiguation

Abstract—Natural language understanding involves the simultaneous consideration of a large number of different sources of information. Traditional methods employed in language analysis have focused on developing powerful formalisms to represent syntactic or semantic structures along with rules for transforming language into these formalisms. However, they make use of only small subsets of knowledge. This article will describe how to use the whole range of information through a neurosymbolic architecture which is a hybridization of a symbolic network and subsymbol vectors generated from a connectionist network. Besides initializing the symbolic network with prior knowledge, the subsymbol vectors are used to enhance the system’s capability in disambiguation and provide flexibility in sentence understanding. The model captures a diversity of information including word associations, syntactic restrictions, case-role expectations, semantic rules and context. It attains highly interacti...

• Type: Journal
• Year: 1998
• Where: TNN
• Authors: Samuel W. K. Chan, James Franklin
• Keywords: Actual Semantic Inferences, Subsymbol Vectors, Symbolic Network, TNN 1998