Computer Science Department
School of Computer Science, Carnegie Mellon University
The Role of Background Knowledge in Sentence Processing
The model is implemented in the ACT-R framework and offers a scalable solution to the problem of language comprehension: its performance (in terms of speed and accuracy) is roughly invariant to the number of facts held in the long-term memory. Its predictions match data from psycholinguistic studies with human subjects. Specifically, the sentence-processing model can simulate the comprehension and verification of metaphoric and literal sentences, metaphor-position effects on sentence comprehension, semantic illusions and their dependence on semantic similarity between the distortion and the undistorted term. The products of the sentence-processing model can explain the pattern of sentence recall in text-memory experiments.
This dissertation also explores the modeling alternatives faced by the design of a sentence-processing model. I show that, to achieve comprehension speed comparable to that of humans, a model must minimize the explicit search process and rely on semantic associations among words. I also investigate how the representation chosen for propositions and meanings affects the comprehension process in a production-system framework such as ACT-R.