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CLEAVER: Hierarchical Text Partitioning & Analysis

A novel, hierarchical approach to English text categorization and partitioning that focuses on independent semantic value, distinguishing itself from traditional syntactic parsers.

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Inferencing Mental Attitudes With Generative AI

An evaluation of the ability of Large Language Models to infer mental attitudes (beliefs, desires, intentions) from text, a crucial component of human cognition.

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Deep Learning Systems for Interpretable Cognitive Modeling

Research on developing transparent deep learning architectures that can model human cognitive processes while remaining interpretable to researchers.

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Understanding the Complex Landscape of Student AI Use in Writing

An exploration of how students integrate AI tools into their writing processes and the implications for education and assessment.

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NSF GRFP: Interpretable Reading Comprehension Models

A research proposal focused on developing interpretable AI models for reading comprehension that provide insights into both machine and human understanding.

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Leveraging Hierarchical Graph Attention Networks for Mental Representation Inferencing

Research on using graph-based neural networks to model and infer human mental representations from text and discourse.

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