ABSTRACT
This paper describes a computational model of semantic processing in natural language discourse understanding based on the distribution of knowledge over multiple spaces as proposed by Fauconnier (1985), Dinsmore (1987a), Kamp (1980), Johnson-Laird (1985) and others. Among the claims made about such a partitioned representation of knowledge are the following: First, it promotes a more direct, more natural mapping from surface discourse sentence to internal representation. Second, it supports more efficient reasoning and retrieval processes over that internal representation. Finally, it provides an accurate account of many of the most recalcitrant problems in natural language discourse understanding. Among these are implicit information, presupposition, referential opacity, tense and aspect, and common-sense reasoning in complex domains.
The model identifies two fundamental levels of semantic processing: contextualization, in which an appropriate space for assimilating the information conveyed in a discourse sentence is located, and construction, in which the information is actually assimilated into that space. Contextualization allows the full semantics of the discourse to be realized implicitly in the internal representation. It also accounts for the use of moods, tenses, and various adverbials in discourse. The interaction of the contextualization processes with the semantics of aspectual operators provides an account of the discourse use of aspect.
