This is a non-exhaustive list of peer-reviewed publications. For the full list, please refer to my Google Scholar.
Publications
2025
Scholarly publications
Teijema, J. J., Ribeiro, G., Seuren, S., Anadria, D., Bagheri, A., & van de Schoot, R. (2025). Simulation-based active learning for systematic reviews: A scoping review of literature. Journal of Information Science. Advance online publication. [DOI]
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]
2024
Scholarly publications
van Dalfsen, A., Karsdorp, F., Bagheri, A., Stronks, E., Mentink, D., & van Engelen, T. (2024). Direct and Indirect Annotation with Generative AI: A Case Study into Finding Animals and Plants in Historical Text. In Computational Humanities Research 2024: Proceedings of the Computational Humanities Research Conference 2024 Aarhus, Denmark, December 4-6, 2024. (pp. 1053-1074). CEUR WS. https://ceur-ws.org/Vol-3834/ [Portal]
Bron, M. P., Greijn, B., Coimbra, B. M., van de Schoot, R., & Bagheri, A. (2024). Combining Large Language Model Classifications and Active Learning for Improved Technology-Assisted Review. CEUR Workshop Proceedings, 3770, 77-95. [Portal]
Fivez, P., Daelemans, W., Van de Cruys, T., Kashnitsky, Y., Chamezopoulos, S., Mohammadi, H., Giachanou, A., Bagheri, A., Poelman, W., Vladika, J., Ploeger, E., Bjerva, J., Matthes, F., & van Halteren, H. (2024). The CLIN33 Shared Task on the Detection of Text Generated by Large Language Models. Computational Linguistics in the Netherlands Journal, 13, 233-259. https://clinjournal.org/clinj/article/view/182 [Portal]
Lim, Y. M. F., Asselbergs, F. W., Bagheri, A., Denaxas, S., Tay, W. T., Voors, A., Lam, C. S. P., Koudstaal, S., Grobbee, D. E., & Vaartjes, I. (2024). Eligibility of Asian and European registry patients for phase III trials in heart failure with reduced ejection fraction. ESC heart failure, 11(6), 3559-3571. [DOI] [Portal]
Qi, Q., Bagheri, A., Hessen, D., & Van der Heijden, P. G. M. (2024). A comparison of correspondence analysis with PMI-based word embedding methods. arXiv. [DOI] [Repository]
2023
Scholarly publications
Teijema, J. J., Hofstee, L., Brouwer, M., de Bruin, J., Ferdinands, G., de Boer, J., Vizan, P., van den Brand, S., Bockting, C., van de Schoot, R., & Bagheri, A. (2023). Active learning-based systematic reviewing using switching classification models: the case of the onset, maintenance, and relapse of depressive disorders. Frontiers in Research Metrics and Analytics, 8, Article 1178181. [DOI] [Portal]
Mohammadi, H., Giachanou, A., & Bagheri, A. (2023). Towards Robust Online Sexism Detection: A Multi-Model Approach with BERT, XLM-RoBERTa, and DistilBERT for EXIST 2023 Tasks. In Working Notes of the Conference and Labs of the Evaluation Forum (CLEF 2023) (pp. 1000-1011). Article 085 (CEUR Workshop Proceedings; Vol. 3497). CEUR WS. https://ceur-ws.org/Vol-3497/ [Repository]
Teijema, J. J., Hofstee, L., Brouwer, M., de Bruin, J., Ferdinands, G., de Boer, J., Vizan, P., van den Brand, S., Bockting, C., van de Schoot, R., & Bagheri, A. (2023). Active learning-based systematic reviewing using switching classification models: the case of the onset, maintenance, and relapse of depressive disorders. Frontiers in Research Metrics and Analytics, 8, Article 1178181. [DOI] [Repository]
Fang, Q., Giachanou, A., Bagheri, A., Boeschoten, L., van Kesteren, E. J., Kamalabad, M. S., & Oberski, D. L. (2023). On Text-based Personality Computing: Challenges and Future Directions. In Findings of the Association for Computational Linguistics, ACL 2023 (pp. 10861-10879). (Proceedings of the Annual Meeting of the Association for Computational Linguistics). Association for Computational Linguistics. [DOI] [Repository]
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]
Ferdinands, G., Schram, R., de Bruin, J., Bagheri, A., Oberski, D. L., Tummers, L., Teijema, J. J., & van de Schoot, R. (2023). Performance of active learning models for screening prioritization in systematic reviews: a simulation study into the Average Time to Discover relevant records. Systematic Reviews , 12(1), Article 100. [DOI] [Repository]
Professional publications
van Leeuwen, A., Bagheri, A., Volker, T., & van Brakel, C. (2023). Verkenning van de inzet van topic modelling bij het analyseren van schrijfopdrachten. Tijdschrift voor Hoger Onderwijs, 41(2), 84-98. [DOI] [Repository]
2022
Scholarly publications
Afsharizadeh, M., Ebrahimpour-Komleh, H., Bagheri, A., & Chrupala, G. (2022). A Survey on Multi-document Summarization and Domain-Oriented Approaches. Journal of Information Systems and Telecommunication, 10(37), 68-79. [DOI] [Repository]
de Jong, D., & Bagheri, A. (2022). The Case of Imperfect Negation Cues: A Two-Step Approach for Automatic Negation Scope Resolution. In P. Rosso, V. Basile, R. Martínez, E. Métais, & F. Meziane (Eds.), Natural Language Processing and Information Systems: 27th International Conference on Applications of Natural Language to Information Systems, NLDB 2022, Valencia, Spain, June 15–17, 2022, Proceedings (1 ed., pp. 413-424). (Lecture Notes in Computer Science; Vol. 13286 ). Springer. [DOI] [Repository]
Teijema, J., Hofstee, L., Brouwer, M., Bruin, J. D., Ferdinands, G., Boer, J. D., Siso, P. V., Brand, S. A. G. E. V. D., Bockting, C., Schoot, R. V. D., & Bagheri, A. (2022). Active learning-based Systematic reviewing using switching classification models: the case of the onset, maintenance, and relapse of depressive disorders. PsyArXiv. [DOI] [Repository]
2021
Scholarly publications
Yang, Z., Bagheri, A., & Van der Heijden, P. G. M. (2021). Neural Networks for Latent Budget Analysis of Compositional Data. (pp. 1-20). arXiv. [DOI] [Repository]
Felix, S. E. A., Bagheri, A., Ramjankhan, F. R., Spruit, M. R., Oberski, D., De Jonge, N., Van Laake, L. W., Suyker, W. J. L., & Asselbergs, F. W. (2021). A data mining-based cross-industry process for predicting major bleeding in mechanical circulatory support. European Heart Journal - Digital Health, 2(4), 635-642. [DOI] [Repository]
Boverhof, B.-J., van de Schoot, R., & Bagheri, A. (2021). ASReview CNN HPO plugin. Software, Zenodo. [DOI]
Teijema, J., de Bruin, J., van de Schoot, R., Hofstee, L., & Bagheri, A. (2021). ASReview wide doc2vec plugin. Software, Zenodo. [DOI]
Vizán Siso, P., de Bruin, J., van de Schoot, R., Hofstee, L., & Bagheri, A. (2021). Code repository for: "Evaluation of the performance of neural network models and classical models in the context of Active Learning for Systematic Reviewing". Software [DOI]
Verberg, G., de Bruin, J., van de Schoot, R., Hofstee, L., & Bagheri, A. (2021). Scripts for a simulation on the effect of dataset size on neural network performance for active learning applied to systematic reviewing. Software, Zenodo. [DOI]
Teijema, J., de Bruin, J., van de Schoot, R., Hofstee, L., & Bagheri, A. (2021). ASReview model switcher plugin. Software, Zenodo. [DOI]
Bagheri, A., Groenhof, T. K. J., Asselbergs, F. W., Haitjema, S., Bots, M. L., Veldhuis, W. B., De Jong, P. A., & Oberski, D. L. (2021). Automatic Prediction of Recurrence of Major Cardiovascular Events: A Text Mining Study Using Chest X-Ray Reports. Journal of Healthcare Engineering, 2021, 1-11. Article 6663884. [DOI] [Repository]
Boeschoten, L., van Kesteren, E.-J., Bagheri, A., & Oberski, D. L. (2021). Achieving Fair Inference Using Error-Prone Outcomes. International Journal of Interactive Multimedia and Artificial Intelligence, 6(5), 9-15. [DOI] [Repository]
Sammani, A., Bagheri, A., van der Heijden, P. G. M., Te Riele, A. S. J. M., Baas, A. F., Oosters, C. A. J., Oberski, D., & Asselbergs, F. W. (2021). Automatic multilabel detection of ICD10 codes in Dutch cardiology discharge letters using neural networks. npj Digital Medicine, 4(1), 1-10. Article 37. [DOI] [Repository]
2020
Scholarly publications
Boeschoten, L., van Kesteren, E., Bagheri, A., & Oberski, D. L. (2020). Fair inference on error-prone outcomes. (pp. 1-14). arXiv. [DOI] [Repository]
Bagheri, A., Groenhof, T. K. J., Veldhuis, W. B., Jong, P. A. D., Asselbergs, F. W., & Oberski, D. L. (2020). Multimodal Learning for Cardiovascular Risk Prediction using EHR Data. (pp. 1-8). arXiv. [DOI] [Repository]
Ferdinands, G., Schram, R., Bruin, J. D., Bagheri, A., Oberski, D. L., Tummers, L., & Schoot, R. V. D. (2020, Sept 16). Active learning for screening prioritization in systematic reviews - A simulation study. OSFPREPRINTS. [DOI] [Repository]
van de Leur, R. R., Boonstra, M. J., Bagheri, A., Roudijk, R. W., Sammani, A., Taha, K., Doevendans, P. A. F. M., van der Harst, P., van Dam, P. M., Hassink, R. J., van Es, R., & Asselbergs, F. W. (2020). Big data and artificial intelligence: Opportunities and threats in electrophysiology. Arrhythmia & Electrophysiology Review, 9(3), 146-154. [DOI] [Repository]
Bagheri, A., Sammani, A., van der Heijden, P. G. M., Asselbergs, F. W., & Oberski, D. L. (2020). ETM: Enrichment by topic modeling for automated clinical sentence classification to detect patients’ disease history. Journal of Intelligent Information Systems, 55(2), 329-349. [DOI] [Repository]
Bagheri, A., Sammani, A., van der Heijden, P. G. M., Asselbergs, F. W., & Oberski, D. L. (2020). Automatic ICD-10 Classification of Diseases from Dutch Discharge Letters. In In conjunction with the 13th International Joint Conference on Biomedical Engineering Systems and Technologies - BIOSTEC 2020 https://www.insticc.org/node/TechnicalProgram/biostec/2020/presentationDetails/93726 [Repository]
Bagheri, A., Sammani, A., Oberski, D. L., & Asselbergs, F. W. (2020). Multi-label ICD Classification of Dutch Hospital Discharge Letters. https://clin30.sites.uu.nl/accepted-submissions/
2019
Scholarly publications
Bagheri, A., Oberski, D. L., Sammani, A., van der Heijden, P. G. M., & Asselbergs, F. W. (2019). SALTClass: classifying clinical short notes using background knowledge from unlabeled data. [DOI] [Repository]
Sedighi, Z., Ebrahimpour-Komleh, H., Bagheri, A., & Kosseim, L. (2019). Opinion Spam Detection with Attention-Based Neural Networks. In The Thirty-Second International Florida Artificial Intelligence Research Society Conference (FLAIRS-32) AAAI Press. https://aaai.org/ocs/index.php/FLAIRS/FLAIRS19/paper/view/18311 [Repository]
Sammani, A., Jansen, M., Linschoten, M., Bagheri, A., & Asselbergs, F. W. (2019). UNRAVEL: big data analytics research data platform to improve care of patients with cardiomyopathies using routine electronic health records and standardised biobanking. Netherlands Heart Journal, 27, 426–434. [DOI] [Repository]
Bagheri, A. (2019). Integrating word status for joint detection of sentiment and aspect in reviews. Journal of Information Science, 45(6), 736-755. [DOI] [Repository]
2018
Scholarly publications
Asgarnezhad, R., Monadjemi, S. A., Soltanaghaei, M., & Bagheri, A. (2018). SFT: A model for sentiment classification using supervised methods on twitter. Journal of Theoretical and Applied Information Technology, 96(8), 2242-2251.
Afsharizadeh, M., Ebrahimpour-Komleh, H., & Bagheri, A. (2018). Query-oriented text summarization using sentence extraction technique. In 2018 4th International Conference on Web Research, ICWR 2018 (pp. 128-132). (2018 4th International Conference on Web Research, ICWR 2018). IEEE. [DOI]
Sedighi, Z., Ebrahimpour-Komleh, H., & Bagheri, A. (2018). RLOSD: Representation learning based opinion spam detection. In Proceedings - 3rd Iranian Conference on Signal Processing and Intelligent Systems, ICSPIS 2017 (pp. 74-80). (Proceedings - 3rd Iranian Conference on Signal Processing and Intelligent Systems, ICSPIS 2017; Vol. 2017-December). IEEE. [DOI]
2016
Scholarly publications
Zaghian, A., & Bagheri, A. (2016). A combined model of clustering and classification methods for preserving privacy in social networks against inference and neighborhood attacks. International Journal of Security and its Applications, 10(1), 95-102. [DOI]
2014
Scholarly publications
Bagheri, A., & Saraee, M. (2014). Persian sentiment analyzer: A framework based on a novel feature selection method. International Journal of Artificial Intelligence, 12(2), 115-129.
Bagheri, A., Saraee, M., & De Jong, F. (2014). ADM-LDA: An aspect detection model based on topic modelling using the structure of review sentences. Journal of Information Science, 40(5), 621-636. [DOI]
2013
Scholarly publications
Bagheri, A., Saraee, M., & de Jong, F. (2013). Latent Dirichlet Markov allocation for sentiment analysis. In In Proceeding of the Fifth European Conference on Intelligent Management Systems in Operations (pp. 90-96) https://ris.utwente.nl/ws/files/5511169/IMSIO_Latent_Dirichlet_Markov_Allocation.pdf
Bagheri, A., Saraee, M., & De Jong, F. (2013). Care more about customers: Unsupervised domain-independent aspect detection for sentiment analysis of customer reviews. Knowledge-Based Systems, 52, 201-213. [DOI]
Bagheri, A., Saraee, M., & De Jong, F. (2013). An unsupervised aspect detection model for sentiment analysis of reviews. In Natural Language Processing and Information Systems - 18th International Conference on Applications of Natural Language to Information Systems, NLDB 2013, Proceedings (pp. 140-151). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 7934 LNCS). [DOI]
Saraee, M., & Bagheri, A. (2013). Feature selection methods in persian sentiment analysis. In Natural Language Processing and Information Systems - 18th International Conference on Applications of Natural Language to Information Systems, NLDB 2013, Proceedings (pp. 303-308). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 7934 LNCS). [DOI]
Bagheri, A., Saraee, M., & De Jong, F. (2013). Sentiment classification in Persian: Introducing a mutual information-based method for feature selection. In 2013 21st Iranian Conference on Electrical Engineering, ICEE 2013 Article 6599671 (2013 21st Iranian Conference on Electrical Engineering, ICEE 2013). [DOI]
2011
Scholarly publications
Moghimi, M., Saraee, M. H., & Bagheri, A. (2011). Modeling of batch annealing process using data mining techniques for cold rolled steel sheets. In 2011 IEEE International Conference on Mechatronics, ICM 2011 - Proceedings (pp. 277-281). Article 5971295 (2011 IEEE International Conference on Mechatronics, ICM 2011 - Proceedings). [DOI]
Saraee, M., Moghimi, M., & Bagheri, A. (2011). Modeling batch annealing process using data mining techniques for cold rolled steel sheets. In Proceedings of the 1st International Workshop on Data Mining for Service and Maintenance, KDD4Service 2011 - Held in Conjunction with SIGKDD'11 (pp. 18-22). (Proceedings of the 1st International Workshop on Data Mining for Service and Maintenance, KDD4Service 2011 - Held in Conjunction with SIGKDD'11). [DOI]
2009
Scholarly publications
Norouzzadeh, M. S., Bagheri, A., & Saraee, M. H. (2009). Web search personalization: A fuzzy adaptive approach. In Proceedings - 2009 2nd IEEE International Conference on Computer Science and Information Technology, ICCSIT 2009 (pp. 143-148). Article 5234590 (Proceedings - 2009 2nd IEEE International Conference on Computer Science and Information Technology, ICCSIT 2009). [DOI]
2008
Scholarly publications
Bagheri, A., Akbarzadeh, M., & Saraee, M. (2008). Finding shortest path with learning algorithms. International Journal of Artificial Intelligence, 1. http://www.ceser.in/ceserp/index.php/ijai/article/view/779