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Contextual effects in the choice of referring expressions for visually presented entities (CORE)

Contextual effects in the choice of referring expressions for visually presented entities (CORE)
Grant of the Spanish Research Agency (2021-2025). CORE aims at providing a better understanding of how humans choose referring expressions, depending on both online contextual factors and on the implicit semantic organization of alternatives.

AEI, PID2020-112602GB-I00 (2021-2025), PI Louise McNally, co-PI Gemma Boleda.

People use language to talk about the world, that is, to refer; accordingly, language offers a very rich set of resources for reference. For example, in any given context, a speaker can choose between a more or less specific expression (the dog, the small dog, the chihuahua), or between expressions that convey complementary information about the referent (the woman, the skier). Which referring expression (RE) a speaker chooses on a given occasion depends on various semantic and pragmatic factors. The theoretical goal of the CORE project is to contribute to better understanding the following specific factors and their interaction in RE choice in context, including the set of general principles that intervene in efficient communication, the contextually salient properties of the entity being referred to and features of its immediate environment that influence successful reference, and the implicit semantic organization of RE alternatives and the conventionalized division of labor between them, especially organization based on implicative semantic relations and alternative cross-classifications which highlight different properties of the referred to entities (e.g., woman vs. skier, or variation in the use of noun classifiers in languages such as Mandarin Chinese). Our empirical goal is to study RE choice under more naturalistic conditions than has previously been done. To combine these two broad goals in a single, feasible project, we make the practical decision of centering our attention on reference to single physical entities in visual contexts. We takes as an empirical starting point the ManyNames dataset, the result of a large-scale collection of RE choices for naturalistic images (Silberer et al. 2020).