A Theory of Interaction and Independence in Sentence Understanding

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Mahesh, Kavi
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Developing a complete and well-specified computational model of human language processing is a difficult problem. Natural language understanding requires the application of many different kinds of knowledge such as syntactic, semantic, and conceptual knowledge. To account for the variety of constructs possible in natural languages and to explain the variety of human behavior in sentence understanding, each kind of knowledge must be applicable independently of others. However, in order to efficiently resolve the many kinds of ambiguities that abound in natural languages, the sentence processor must integrate information available from different knowledge sources as soon as it can. Such early commitment in ambiguity resolution calls for an ability to recover from possible errors in commitment. In this work, we propose a unified-process, multiple knowledge-source model of sentence understanding that satisfies all the constraints above. In this model, syntactic, semantic, and conceptual knowledge are represented separately but in the same form. The single unified process utilizes all knowledge sources to process a sentence. The unified process can resolve structural as well as lexical ambiguities and recover from errors it might make. We show that this model can account for a range of human sentence processing behaviors by producing seemingly autonomous behavior at times and interactive behaviors at other times. It is efficient since it supports interaction between syntactic, semantic, and conceptual processing. Moreover, the model aids portability between domains by separating domain-specific knowledge from general linguistic knowledge. We also present an early commitment, expectation-driven, bottom-up theory of syntactic processing that permits us to unify syntactic processing with semantic processing. We show several illustrative examples of ambiguity resolution and error recovery processed by our prototype implementation of the theory in a program called COMPERE (Cognitive Model of Parsing and Error Recovery).
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