Cognitive Science Colloquium
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Title: Frame-Based Models of Linguistic Cognition for Social Impact: Insights from the Data to Stop GBV Initiative
Abstract: This talk explores the potential of domain-specific framenets in the semantic analysis of open-text fields in e-medical records, aiming to identify early patterns of gender-based violence (GBV) before escalation. I compare the performance of a frame-based AI model with two alternatives: one relying solely on demographic data and another leveraging a large language model (LLM). The results highlight the superior performance of the frame-based approach, emphasizing the value of human-curated, perspectivized models of linguistic cognition. These findings underline the critical role of interpretable and responsible AI in addressing pressing societal challenges, such as GBV prevention.