David Bourget (Western Ontario)
David Chalmers (ANU, NYU)
Rafael De Clercq
Ezio Di Nucci
Jonathan Jenkins Ichikawa
Jack Alan Reynolds
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I compare several types of knowledge-based and knowledge-poor approaches to anaphora and ellipsis resolution. The former are able to capture ﬁne-grained distinctions that depend on lexical meaning and real world knowledge, but they are generally not robust. The latter show considerable promise for yielding wide coverage systems. However, they consistently miss a small but signiﬁcant subset of cases that are not accessible to rough-grained techniques of intepretation. I propose a sequenced model which ﬁrst applies the most computationally eﬃcient and inexpensive methods to resolution and then progresses successively to more costly techniques to deal with cases not handled by previous modules. Conﬁdence measures evaluate the judgements of each component in order to determine which instances of anaphora or ellipsis are to be passed on to the next, more ﬁne-grained subsystem
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