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Julia Constanze Hockenmaier

Professor of Computer Science

Additional Campus Affiliations

Professor, Siebel School of Computing and Data Science
Professor, Coordinated Science Lab
Professor, Linguistics
Professor, Center for Digital Agriculture, National Center for Supercomputing Applications (NCSA)

Recent Publications

Haldar, R., & Hockenmaier, J. (2024). Analyzing the Performance of Large Language Models on Code Summarization. In N. Calzolari, M.-Y. Kan, V. Hoste, A. Lenci, S. Sakti, & N. Xue (Eds.), 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation, LREC-COLING 2024 - Main Conference Proceedings (pp. 995-1008). (2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation, LREC-COLING 2024 - Main Conference Proceedings). European Language Resources Association (ELRA).

Jung, Y., Hockenmaier, J., & Golparvar-Fard, M. (2024). Feasibility analysis on the use of NLP-based schedule analytics for 4D project planning and controls. In Y. Turkan, J. Louis, F. Leite, & S. Ergan (Eds.), Computing in Civil Engineering 2023: Data, Sensing, and Analytics - Selected Papers from the ASCE International Conference on Computing in Civil Engineering 2023 (pp. 42-50). (Computing in Civil Engineering 2023: Data, Sensing, and Analytics - Selected Papers from the ASCE International Conference on Computing in Civil Engineering 2023). American Society of Civil Engineers. https://doi.org/10.1061/9780784485224.006

Jung, Y., Hockenmaier, J., & Golparvar-Fard, M. (2024). Transformer language model for mapping construction schedule activities to uniformat categories. Automation in Construction, 157, Article 105183. https://doi.org/10.1016/j.autcon.2023.105183

Canby, M. E., & Hockenmaier, J. (2023). A Framework for Bidirectional Decoding: Case Study in Morphological Inflection. In Findings of the Association for Computational Linguistics: EMNLP 2023 (pp. 4485-4507). (Findings of the Association for Computational Linguistics: EMNLP 2023). Association for Computational Linguistics (ACL). https://doi.org/10.18653/v1/2023.findings-emnlp.297

Cho, I., Jung, Y., & Hockenmaier, J. (2023). SIR-ABSC: Incorporating Syntax into RoBERTa-based Sentiment Analysis Models with a Special Aggregator Token. In Findings of the Association for Computational Linguistics: EMNLP 2023 (pp. 8535-8550). (Findings of the Association for Computational Linguistics: EMNLP 2023). Association for Computational Linguistics (ACL).

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