In this paper, I demonstrate how complex cases of definite reference resolution can be processed within the independently motivated framework of time-constrained memory, and, most importantly, without having to resort to the complex mechanisms assumed by Haddock (1987). The key idea of the solution is to initiate a referent search for the complex definite N P formed by the attachment of a prepositional phrase to a definite noun phrase.

Existing models of sentence comprehension typically adopt a deterministic approach that decides on the correct parse of a sentence. In essence, these models consist of algorithms that statically capture a priori rules for disambiguation and seldom take into account the context of interpretation. Also, their deterministic nature eliminates the possibility of recognizing a genuine ambiguity. We argue that the very essence of the quantitative model of memory we have developed, that is, its time-constrained nature, allows for a non- deterministic contextual approach to structural disambiguation. In this paper, w e focus specifically on the problem of PP (Prepositional Phrase) Attachment. More precisely, w e contend that a solution to this problem depends on the use of both a massively parallel time-constrained architecture and a quantitatively-defined context.

Information-processing models for comprehension typically regard text as the depository of a single determinate meaning, placed in it by the writer. Conversely, a reader-based approach views meaning as constituted by the interactions between an individual and a text. From a computational standpoint, reader-based understanding suggests abandoning models which depend on a priori rules of interpretation and limiting the design of an algorithm to the quantitative aspects of text comprehension. I propose that the perception of subject matter be viewed as a race process where the generation of bridging inferences and expectations is partly controlled by quantitative factors (such as the delay for memory retrieval) which emphasize the invisible but omnipresent role of time during reading.

In this paper we criticize existing computational models of lexicon for assuming that for every word there is a fixed number of word sense that must be searched for the proper meaning of that word in a context. We reject this sense enumerative view and argue for a different model of lexicon in which the effects of context are not limited to selecting a word sense, and selected senses can be contextually modulated. W e also explain how patterns of contextual effects could evolve in an exemplar-based fashion. A prototype implementation of this model is also discussed.