Developing a complete and well-speciied computational model of human language processing is a diicult problem. Natural language understanding requires the application of many diierent 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 eeciently resolve the many kinds of ambiguities that abound in natural languages, the sentence processor must integrate information available from diierent 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 uniied-process, multiple knowledge-source model of sentence understanding that satisses all the constraints above. In this model, syntactic, semantic, and conceptual knowledge are represented separately but in the same form. The single uniied process utilizes all knowledge sources to process a sentence. The uniied 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 eecient since it supports interaction between syntactic, semantic, and conceptual processing. Moreover, the model aids portability between domains by separating domain-speciic 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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