Seminář ÚL

In this talk, I will give a brief overview of my research on language in aphasia. I will start with the relationship between aphasiology and aphasic data and linguistics. This will be followed by three case studies. In the first case study, I will show how entrenchment and chunking modulate fluency, using prepositional phrases as an example. This study shows how a usage-based approach to language can complement approaches that focus more on the role of structural complexity in explaining linguistic behaviour in aphasia. The second study shows how a linguistically informed analysis can provide a more systematic and principled description of aphasic data. Specifically, I will present a description of verb and arguments structure production in aphasia, using the perspective of Construction Grammar and Frame Semantics. The last part of the talk will be dedicated to a new project in which I will focus on Czech inflectional morphology in speakers with aphasia and the possibilities of applying computational models of learning on this data.

I am preparing a study on semantic networks based on word vectors trained on the Clinical English Gillam corpus (Gillam & Pearson 2004) containing narratives of children with typical language development and specific language impairment (SLI). The aim is to analyse the structure of those semantic networks at different stages of acquisition with the hypothesis that a 'small-world structure', characterized by prominent hub words with many connections and local clusters of closely related words, will be found in typically developing children while a network with less dominant hubs and more evenly linked nodes will be found for children with SLI. Small-world network allows, in theory, effective search strategies in local clusters as well as across distant domains via the hub nodes (Watts & Strogatz 1998; Steyvers & Tenenbaum 2005) which is why I assume that its disruption should occur in SLI. Special focus will lie on whether this network measure would be able to distinguish typical and SLI children with similar mean length of utterance in which case this network measure would outperform a traditional psycholinguistic measure used to diagnose SLI (Rice et al. 2010).

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