Annie Lamar

Assistant Professor
Pronouns:
she/her/hers
Area:
archaic Greek; Homeric epic; computational linguistics; digital humanities; machine-learning
Office:
HSSB 4045
Email:
aklamar@ucsb.edu

About:

Annie K. Lamar specializes in low-resource computational linguistics with special interests in ancient Mediterranean languages and studies.  Recent projects include geospatial approaches to catastrophe narratives from the Mediterranean, new approaches to measuring variability in vector spaces across differently resourced languages, and contributions to computational humanities toolkits. Lamar is internationally published for her work in digital humanities, computational linguistics, and machine learning.

Within Classics, Lamar specializes in archaic Greek poetry and Homeric epic. Her research in the field is primarily concerned with computational models of orally-composed poetry and oral artifacts. Her in-progress monograph introduces a novel approach to the Homeric formula, offering a computational model to excavate the mechanism of formulaic composition.

Lamar holds a PhD in Classics from Stanford University, an MA in Education Data Science from the Stanford Graduate School of Education, and both a BA in Classical Languages and a BS in Computer Science from the University of Puget Sound. Currently, Lamar is an Assistant Professor of Classics and the director of the Low-Resource Language (LOREL) Lab at University of California, Santa Barbara.

Publications:

  • Lamar, Annie K.; Castle, Rick; Chappell, Carissa; Schoinoplokaki, Emmanouela; Seet, Allene M.; Shilo, Amit; Nahas, Chloe. 2025. “Cognitive Geographies of Catastrophe Narratives: Georeferenced Interview Transcriptions as Language Resources for Models of Forced Displacement.” Proceedings of the 1st International Workshop on Nakba Narratives as Language Resources. Part of The 31st International Conference on Computational Linguistics. In press.
  • Dubit, Rachel E.; Lamar, Annie K. 2024. “Era- and Genre- Specific Stop Word Lists for Low-Resource Computational Research: A Classical Latin Exemplum.” Journal of Open Humanities Data Vol. 10, No. 1: pp. 53. https://doi.org/10.5334/johd.246.
  • Lamar, Annie K.; Kaya, Zeyneb. 2023. “Measuring the Impact of Data Augmentation Algorithms for Extremely Low-Resource NMT.” In Proceedings of the Sixth Workshop on Technologies for Machine Translation of Low-Resource Languages (LoResMT23), pp. 101-109. Association for Computational Linguistics. Available here.
  • Lamar, Annie K.; Chambers, America. 2020. “Generating Metrically Accurate Homeric Poetry with Recurrent Neural Networks.” International Journal of Transdisciplinary Artificial Intelligence Vol. 2, No. 1: pp 1-25. DOI: 10.35708/TAI1869-126247.