Abstract
The phenomenon of ‘donkey anaphora’ has been much discussed in the linguistic semantics literature (e.g., see Carlson & Pelletier, 1995; Chierchia, 1995), and has given rise to new theories of logical form based on various kinds of ‘dynamic semantics’.1 Although these theories appear to account for donkey anaphora in a natural way, they have not had much impact so far on knowledge representation in AI. This is surprising since — as I will argue — much of the ‘common-sense’ knowledge needed for ordinary reasoning and language understanding characteristically involves donkey anaphora, when this knowledge is expressed in words. The reason may be that the development of knowledge representations has been influenced more by the need for efficient inference than the need for capturing linguistically conveyed information.
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Schubert, L. (1999). Dynamic Skolemization. In: Bunt, H., Muskens, R. (eds) Computing Meaning. Studies in Linguistics and Philosophy, vol 73. Springer, Dordrecht. https://doi.org/10.1007/978-94-011-4231-1_12
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