Málvísindastofnun: Maria Copot

Maria Copot, fræðimaður við University of Surrey, flytur fyrirlestur á vegum Málvísindastofnunar sem nefnist „How the input determines the granularity of morphological generalisation.“

Fyrirlesturinn verður haldinn í fyrirlestrasalnum í Eddu, þriðjudaginn 20. október kl. 14:00. Hann er opinn öllum.

Um fyrirlesarann

Dr. Maria Copot is a British Academy International Fellow at the Surrey Morphology Group (University of Surrey). Their research asks how knowledge of words is organised in speakers' minds, and how that organisation interacts with memory, cognition and the social and communicative contexts in which language is used. The work approaches these questions empirically, combining behavioural experiments, computational modelling and large-scale statistical analysis to test claims from morphological theory, with a particular focus on paradigmatic organisation, defectiveness and overabundance.

Um fyrirlesturinn:

Morphological knowledge can be characterised at multiple levels of abstraction: the same facts about the lexicon can be stated as a single schema per inflectional macro-class, as intermediate sub-schemas over lexeme sets that share a given property, or as stored individual forms. This is usually cast as a choice of cognitive architecture: pure prototype, pure exemplar, or intermediate/multi-level systems. We reframe the question of the correct level of abstraction to model morphological knowledge as not one of architecture, but one where optimal level of abstraction is itself the variable of interest, to be derived from properties of the data. Under a resource-rational view (Anderson, 1991), humans build as much representational structure as the evidence justifies and no more, so the optimal granularity is inferred from the input, shifts as evidence accumulates, and may differ across regions of the lexicon. We treat the question of how we partition and represent lexical knowledge as a categorisation problem and model it as Bayesian nonparametric inference in which granularity is a free parameter. We model trial-by-trial performance in an artificial-language experiment on plural allomorphy with exceptions with models that make different assumptions about granularity. On the basis of the results, we argue that granularity is thus productively thought of not a property of the morphological architecture to be discovered but a quantity inferred from evidence. The method speaks directly to theories of morphology which have an architecture where all levels of abstraction coexist, enabling them to make predictions about which grain a given speaker brings to bear on a particular linguistic task for a particular word.