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Dr. Pablo Mosteiro Romero

Assistant Professor 
Social and Behavioural Sciences
Social Sciences
Methodology and Statistics
Methodology and Statistics
Sjoerd Groenmangebouw
Padualaan 14
Kamer C122
3584 CH Utrecht

Dr. Pablo Mosteiro Romero

Assistant Professor
Methodology and Statistics
p.j.mosteiroromero@uu.nl
Profile CV Publications Research Teaching Contact
Profile CV Publications Research Teaching Contact
Areas of expertise
Research areas

Full list of publications here

 

 

Publications

2025

Scholarly publications

Mosteiro Romero, P., & Blasi, D. (2025). Word boundaries and the morphology-syntax trade-off. In S. Yagi, S. Yagi, M. Sawalha, B. A. Shawar, A. T. AlShdaifat, N. Abbas, & Organizers (Eds.), Proceedings of the New Horizons in Computational Linguistics for Religious Texts (pp. 86-93). Association for Computational Linguistics (ACL). https://aclanthology.org/2025.clrel-1.9/
https://research-portal.uu.nl/ws/files/255693888/2025.clrel-1.9.pdf
Rijcken, E., Zervanou, K., Mosteiro, P., Scheepers, F., Spruit, M., & Kaymak, U. (2025). Machine learning vs. rule-based methods for document classification of electronic health records within mental health care: A systematic literature review. Natural Language Processing, 10, Article 100129. https://doi.org/10.1016/j.nlp.2025.100129
https://research-portal.uu.nl/ws/files/252554600/1-s2.0-S2949719125000056-main.pdf

2024

Scholarly publications

Sogancioglu, G., Mosteiro Romero, P., Salah, A., Scheepers, F. E., & Kaya, H. (2024). Fairness in AI-Based Mental Health: Clinician Perspectives and Bias Mitigation. Proceedings of the Seventh AAAI/ACM Conference on AI, Ethics, and Society, 7, 1390-1400. https://ojs.aaai.org/index.php/AIES/article/view/31732
Quantmeyer, V., Mosteiro Romero, P., & Gatt, A. (2024). How and where does CLIP process negation? In ALVR 2024 (pp. 59-72). Association for Computational Linguistics. https://aclanthology.org/2024.alvr-1.5
https://research-portal.uu.nl/ws/files/237165271/2024.alvr-1.5.pdf
Sarhan, I., Toth, B., Mosteiro, P., & Wang, S. (2024). TaxoCritic: Exploring Credit Assignment in Taxonomy Induction with Multi-Critic Reinforcement Learning. In G. Serasset, H. G. Oliveira, & G. V. Oleskeviciene (Eds.), Proceedings of the Workshop on DLnLD 2024: Deep Learning and Linked Data at LREC-COLING 2024 - Workshop Proceedings (pp. 14-30). (Proceedings of the Workshop on DLnLD 2024: Deep Learning and Linked Data at LREC-COLING 2024 - Workshop Proceedings). European Language Resources Association (ELRA). https://aclanthology.org/2024.dlnld-1.2
https://research-portal.uu.nl/ws/files/227691204/2024.dlnld-1.2.pdf
Rijcken, E., Zervanou, K., Mosteiro Romero, P., Scheepers, F. E., Spruit, M., & Kaymak, U. (2024). Topic Specificity: a Descriptive Metric for Algorithm Selection and Finding the Right Number of Topics. Natural Language Processing, 8, Article 100082. https://doi.org/10.1016/j.nlp.2024.100082
https://research-portal.uu.nl/ws/files/228389412/1-s2.0-S294971912400030X-main.pdf
Grotenhuis, Z., Mosteiro Romero, P., & Leeuwenberg, A. J. M. (2024). Modest performance of text mining to extract health outcomes may be almost sufficient for high-quality prognostic model development. Computers in Biology and Medicine, 170, Article 108014. https://doi.org/10.1016/j.compbiomed.2024.108014
https://dspace.library.uu.nl/bitstream/handle/1874/435643/1-s2.0-S0010482524000982-main.pdf?sequence=1

2023

Scholarly publications

Bagheri, A., Giachanou, A., Mosteiro Romero, P., & Verberne, S. (2023). Natural Language Processing and Text Mining (Turning Unstructured Data into Structured). In F. Asselbergs, S. Denaxas, D. Oberski, & J. Moore (Eds.), Clinical Applications of Artificial Intelligence in Real-World Data (1 ed., pp. 69-93). Springer. https://doi.org/10.1007/978-3-031-36678-9_5
https://dspace.library.uu.nl/bitstream/handle/1874/436378/978-3-031-36678-9_5.pdf?sequence=1
Ito, T., Fang, Q., Mosteiro Romero, P., Gatt, A., & van Deemter, K. (2023). Challenges in Reproducing Human Evaluation Results for Role-Oriented Dialogue Summarization. In The 3rd Workshop on Human Evaluation of NLP Systems (HumEval’23) Association for Computational Linguistics. https://aclanthology.org/2023.humeval-1.9
https://dspace.library.uu.nl/bitstream/handle/1874/436320/2023.humeval-1.9.pdf?sequence=1
Belz, A., Thomson, C., Reiter, E., Abercrombie, G., Alonso-Moral, J. M., Arvan, M., Cheung, J., Cieliebak, M., Clark, E., Deemter, K. V., Dinkar, T., Dušek, O., Eger, S., Fang, Q., Gatt, A., Gkatzia, D., González-Corbelle, J., Hovy, D., Hürlimann, M., ... Yang, D. (2023). Missing Information, Unresponsive Authors, Experimental Flaws: The Impossibility of Assessing the Reproducibility of Previous Human Evaluations in NLP. In The Fourth Workshop on Insights from Negative Results in NLP (pp. 1-10). Association for Computational Linguistics. https://aclanthology.org/2023.insights-1.1
https://dspace.library.uu.nl/bitstream/handle/1874/429997/2023.insights-1.1.pdf?sequence=1
Rijcken, E., Scheepers, F., Zervanou, K., Spruit, M., Mosteiro Romero, P., & Kaymak, U. (2023). Towards Interpreting Topic Models with ChatGPT. In The 20th World Congress of the International Fuzzy Systems Association (IFSA 2023) (pp. 269-275). International Fuzzy Systems Association.
https://dspace.library.uu.nl/bitstream/handle/1874/452617/5862c343-2137-4110-8cde-ed67e9f9a756.pdf?sequence=1
van Buchem, M. M., 't Hart, H., Mosteiro Romero, P., Kant, I. M. J., & Bauer, M. P. (2023). Diagnosis Classification in the Emergency Room Using Natural Language Processing. In Caring is Sharing – Exploiting the Value in Data for Health and Innovation (pp. 815 - 816). (Studies in Health Technology and Informatics; Vol. 302). IOS Press. https://doi.org/10.3233/SHTI230273
https://dspace.library.uu.nl/bitstream/handle/1874/428676/SHTI_302_SHTI230273.pdf?sequence=1

2022

Scholarly publications

Rijcken, E., Zervanou, K., Spruit, M., Mosteiro Romero, P., Scheepers, F. E., & Kaymak, U. (2022). Exploring Embedding Spaces for more Coherent Topic Modeling in Electronic Health Records. In IEEE International Conference on Systems, Man, and Cybernetics (pp. 2669-2674). (Conference Proceedings - IEEE International Conference on Systems, Man and Cybernetics; Vol. 2022-October). IEEE. https://doi.org/10.1109/SMC53654.2022.9945594
https://dspace.library.uu.nl/bitstream/handle/1874/431740/Exploring_Embedding_Spaces_for_more_Coherent_Topic_Modeling_in_Electronic_Health_Records.pdf?sequence=1
Sarhan, I., Mosteiro Romero, P., & Spruit, M. (2022). UU-Tax at SemEval-2022 Task 3: Improving the generalizability of language models for taxonomy classification through data augmentation. In Proceedings of the 16th International Workshop on Semantic Evaluation (SemEval-2022) (pp. 271-281). Association for Computational Linguistics. https://doi.org/10.18653/v1/2022.semeval-1.35
https://dspace.library.uu.nl/bitstream/handle/1874/423204/2022.semeval_1.35.pdf?sequence=1
Mosteiro Romero, P., Kuiper, J., Masthoff, J., Scheepers, F. E., & Spruit, M. (2022). Bias Discovery in Machine Learning Models for Mental Health. Information, 13(5), 1-15. Article 237. https://doi.org/10.3390/info13050237
https://dspace.library.uu.nl/bitstream/handle/1874/420513/information_13_00237_v2.pdf?sequence=1
Rijcken, E., Kaymak, U., Scheepers, F. E., Mosteiro Romero, P., Zervanou, K., & Spruit, M. (2022). Topic Modeling for Interpretable Text Classification From EHRs. Frontiers in Big Data, 5, 1-11. Article 846930. https://doi.org/10.3389/fdata.2022.846930
https://dspace.library.uu.nl/bitstream/handle/1874/420583/fdata_05_846930.pdf?sequence=1
Borger, T., Mosteiro Romero, P., Kaya, H., Rijcken, E., Salah, A., Scheepers, F., & Spruit, M. (2022). Federated learning for violence incident prediction in a simulated cross-institutional psychiatric setting. Expert Systems with Applications, 199, 1-9. Article 116720. https://doi.org/10.1016/j.eswa.2022.116720
https://dspace.library.uu.nl/bitstream/handle/1874/420387/1_s2.0_S0957417422001944_main.pdf?sequence=1

2021

Scholarly publications

Rijcken, E., Scheepers, F. E., Mosteiro Romero, P., Zervanou, K., Spruit, M., & Kaymak, U. (2021). A Comparative Study of Fuzzy Topic Models and LDA in terms of Interpretability. In 2021 IEEE Symposium Series on Computational Intelligence (SSCI) (SSCI 2021) (pp. 1-8). IEEE. https://doi.org/10.1109/SSCI50451.2021.9660139
https://research-portal.uu.nl/ws/files/233446214/A_Comparative_Study_of_Fuzzy_Topic_Models_and_LDA_in_terms_of_Interpretability.pdf
Mosteiro Romero, P., Rijcken, E., Zervanou, K., Kaymak, U., Scheepers, F. E., & Spruit, M. (2021). Machine Learning for Violence Risk Assessment Using Dutch Clinical Notes. Journal of Artificial Intelligence for Medical Sciences, 2( 1-2), 44-54. https://doi.org/10.2991/jaims.d.210225.001
https://dspace.library.uu.nl/bitstream/handle/1874/412240/125953465.pdf?sequence=1

2020

Scholarly publications

Mosteiro Romero, P. J., Rijcken, E., Zervanou, K., Kaymak, U., Scheepers, F., & Spruit, M. (2020). Making sense of violence risk predictions using clinical notes. In Z. Huang, S. Siuly, H. Wang, R. Zhou, & Y. Zhang (Eds.), Health Information Science: 9th International Conference, HIS 2020, Amsterdam, The Netherlands, October 20–23, 2020, Proceedings (pp. 3-14). (Lecture Notes in Computer Science; Vol. 12435). Springer. https://doi.org/10.1007/978-3-030-61951-0_1
https://dspace.library.uu.nl/bitstream/handle/1874/414646/Mosteiro2020_Chapter_MakingSenseOfViolenceRiskPredi.pdf?sequence=1
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