Publications
2025
Scholarly publications
Zgreaban, M., Gatt, A., & Mosteiro Romero, P. (2025). Hit or Be Hit: Tests of (Pre)Compositional Abilities in Vision and Language Models. In C. Wartena, & U. Heid (Eds.), KONVENS : 21th Conference on Natural Language Processing (KONVENS 2025) ; Proceedings of the Conference. Volume 1: Long and Short Papers (Vol. 1, pp. 306-317). Hochschule Hannover. [DOI] [Portal]
Mohammadi, H., Shahedi, T., Mosteiro Romero, P., Poesio, M., Bagheri, A., & Giachanou, A. (2025). Assessing the Reliability of LLMs Annotations in the Context of Demographic Bias and Model Explanation. In Proceedings of the 6th Workshop on Gender Bias in Natural Language Processing (GeBNLP) (pp. 92-104). Article 9 Association for Computational Linguistics. [DOI] [Portal]
Bakx, T., Vlah, Z., Mosteiro Romero, P., & Chisari, E. (2025). COBRA: Optimal Factorization of Cosmological Observables. Data set/Database, Zenodo. [DOI]
Mosteiro Romero, P., Blasi, D., & Paperno, D. (2025). West Germanic noun-noun compounds and the morphology-syntax trade-off. In G. Nicolai, E. Chodroff, F. Frederic, & C. Coltekin (Eds.), Proceedings of the The 22nd SIGMORPHON workshop on Computational Morphology, Phonology, and Phonetics (pp. 15-22). Association for Computational Linguistics. https://aclanthology.org/2025.sigmorphon-main.2 [Portal]
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/ [Portal]
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. [DOI] [Portal]
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 [Portal]
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 [Portal]
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 [Portal]
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. [DOI] [Repository]
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. [DOI] [Repository]
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 [Repository]
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 [Repository]
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. [Portal]
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. [DOI] [Repository]
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. [DOI] [Repository]
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. [DOI] [Repository]
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. [DOI] [Repository]
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. [DOI] [Repository]
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. [DOI] [Repository]
2021
Scholarly publications
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. [DOI] [Repository]
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. [DOI] [Repository]