ML p(r)ior | Uniform Representations for Syntax-Semantics Arbitration

Uniform Representations for Syntax-Semantics Arbitration

9408018 | cmp-lg
Psychological investigations have led to considerable insight into the working of the human language comprehension system. In this article, we look at a set of principles derived from psychological findings to argue for a particular organization of linguistic knowledge along with a particular processing strategy and present a computational model of sentence processing based on those principles. Many studies have shown that human sentence comprehension is an incremental and interactive process in which semantic and other higher-level information interacts with syntactic information to make informed commitments as early as possible at a local ambiguity. Early commitments may be made by using top-down guidance from knowledge of different types, each of which must be applicable independently of others. Further evidence from studies of error recovery and delayed decisions points toward an arbitration mechanism for combining syntactic and semantic information in resolving ambiguities. In order to account for all of the above, we propose that all types of linguistic knowledge must be represented in a common form but must be separable so that they can be applied independently of each other and integrated at processing time by the arbitrator. We present such a uniform representation and a computational model called COMPERE based on the representation and the processing strategy.

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