معرفی
Lea Frermann is a Senior Lecturer and DECRA 2023 Fellow in the School of Computing and Information Systems at the University of Melbourne. Her research focuses on understanding how humans learn about and represent complex and evolving information in large-scale and noisy environments, with applications to developing fairer and more robust automatic systems. She combines methods from natural language processing, machine learning, and computational cognitive modeling in her work.
Her primary research interests include narrative understanding, media framing, word meaning change over centuries, category and feature learning in children, and fairness in natural language processing. She has developed scalable models of category and feature learning from noisy language data and models of historical word meaning change. Her current research focuses on improving automatic understanding of narratives in both fiction and reality, analyzing framing and narrative strategies in biased news stories.
Dr. Frermann's recent publications demonstrate a strong trend toward interdisciplinary research at the intersection of NLP, cognitive science, and social science, with particular emphasis on media framing analysis, bias detection, and narrative understanding. Her work often involves collaborations across multiple institutions and disciplines.
- DECRA Fellow 2023
- Outstanding Paper Award at ACL 2024 for 'Media Framing: A Typology and Survey of Computational Approaches Across Disciplines'
- Best Paper Award at ALTA 2023
She currently supervises multiple PhD students including Chaoyi Xiang, Damian Curran, Matteo Guida, Bryan Chen, Yilin Geng, Gisela Vallejo, Uri Berger, Katie Warburton, and John Xu. Her former students include Sheilla Njoto, Chunhua Liu, Shima Khanezar, and Kemal Kurniawan. She has secured significant research funding, including the DECRA fellowship, supporting her work on narrative understanding and media framing.
Dr. Frermann leads a research group focused on narrative understanding and computational social science, with projects spanning media framing analysis, bias detection in language models, narrative structure in fiction, and cross-cultural communication patterns.
