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This paper presents a connectionist syntactic parser which uses Structure Unification Grammar as its grammatical framework. The parser is implemented in a connectionist architecture which stores and dynamically manipulates symbolic representations, but which can't represent arbitrary disjunction and has bounded memory. These problems can be overcome with Structure Unification Grammar's extensive use of partial descriptions. INTRODUCTION The similarity between connectionist models of computation and neuron computation suggests that a study of syntactic parsing in a connectionist computational architecture could lead to significant insights into ways natural language can be parsed efficiently. Unfortunately, previous investigations into connectionist parsing (Cottrell, 1989,. | A CONNECTIONIST PARSER FOR STRUCTURE UNIFICATION GRAMMAR James B. Henderson Department of Computer and Information Science University of Pennsylvania 200 South 33rd Philadelphia PA 19104 USA henders@linc.cis.upenn.edu ABSTRACT This paper presents a connectionist syntactic parser which uses Structure Unification Grammar as its grammatical framework. The parser is implemented in a connectionist architecture which stores and dynamically manipulates symbolic representations but which can t represent arbitrary disjunction and has bounded memory. These problems can be overcome with Structure Unification Grammar s extensive use of partial descriptions. INTRODUCTION The similarity between connectionist models of computation and neuron computation suggests that a study of syntactic parsing in a connection-ist computational architecture could lead to significant insights into ways natural language can be parsed efficiently. Unfortunately previous investigations into connectionist parsing Cottrell 1989 Fanty 1985 Selman and Hirst 1987 have not been very successful. They cannot parse arbitrarily long sentences and have inadequate grammar representations. However the difficulties with connectionist parsing can be overcome by adopting a different connectionist model of computation namely that proposed by Shastri and Ajjana-gadde 1990 . This connectionist computational architecture differs from others in that it directly manifests the symbolic interpretation of the information it stores and manipulates. It also shares the massive parallelism evidential reasoning ability and neurological plausibility of other connec-tionist architectures. Since virtually all characterizations of natural language syntax have relied heavily on symbolic representations this architecture is ideally suited for the investigation of syntactic parsing. This research was supported by ARO grant DAAL 03-89-C-0031 DARPA grant N00014-90-J-1863 NSF grant IRI 90-16592 and Ben Franklin grant 91S.3078C-1. The .
