Deirdre W. Wheeler

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a b c d d c b a d c b a d c b a Arrows are annotated by the substrucure to which they are applied d c b a Figure 3: Why ?! is weakly Church-Rosser Note that all that was said in this section generalizes beyond CCG derivations to any associative algebra. Given the rightmost subconstituent recovered using the normal form technique above, how should parsing(More)
Deirdre W. Wheeler Department of Linguistics University of Pittsburgh Pittsburgh, PA 15260 The phonological structure of human languages is intricate, yet highly constrained. Through a combination of connectionist modeling and linguistic analysis, we are attempting to develop a computational basis for the nature of phonology. We present a connectionist(More)
This paper describes and evaluates one method of modeling compliance in a wheel-on-leg walking robot. This method assumes that all of the robot’s compliance takes place at the ground contact points, specifically the tires and legs, and that the rest of the robot is rigid. Optimization is used to solve for the displacement of the feet and of the center of(More)
This paper reports on an initial implementation of Lakoff s theory of cognitive phonology in a connectionist network. Standard generative phonological theories require serial application of rules, which results in derivations with numerous intermediate states. This is incompatible with the connectionist goals of psychological and biological plausibility,(More)
This report contains three papers on symbol processing in connectionist networks. The first two, "A Computational Basis for Phonology" and "Rationale for a 'Many Maps' Phonology Machine," present the latest results of our ongoing project to develop a connectionist explanation for the regularities and peculiarities of human phonological behavior. The third(More)
In a previous paper (Touretzky & Wheeler, 1990a) we showed how adding a clustering operation to a connectionist phonology model produced a parallel processing account of certain "iterative" phenomena. In this paper we show how the addition of a second structuring primitive, syllabification, greatly increases the power of the model. We present examples from(More)
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