Daten sicher teilen - Landkarte der Möglichkeiten. Buck, D., Hoffstätter, U., Beck, K., Siegers, P., Linne, M., & Schlücker, F. (2025, Mai). Workshop Daten sicher teilen - Landkarte der Möglichkeiten.
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Datenüberlassung an ein FDZ oder selbst eines gründen? Aufgaben und Strukturen eines FDZ. Siegers, P., & Buck, D. (2025, Mai). Datenüberlassung an ein FDZ oder selbst eines gründen? Aufgaben und Strukturen eines FDZ. Vortrag auf dem Workshop Daten sicher teilen - Landkarte der Möglichkeiten.
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AutoML meets hugging face: Domain-aware pretrained model selection for text classification. Safikhani, P. (2025, April/Mai). AutoML meets hugging face: Domain-aware pretrained model selection for text classification. Poster auf der Konferenz 2025 Conference of the Nations of the Americas Chapter of the Association for Computational Linguistics: Human Language Technologies, Albuquerque, USA.
Abstract
The effectiveness of embedding methods is crucial for optimizing text classification performance in Automated Machine Learning (AutoML). However, selecting the most suitable pre-trained model for a given task remains challenging. This study introduces the Corpus-Driven Domain Mapping (CDDM) pipeline, which utilizes a domain-annotated corpus of pre-fine-tuned models from the Hugging Face Model Hub to improve model selection. Integrating these models into AutoML systems significantly boosts classification performance across multiple datasets compared to baseline methods. Despite some domain recognition inaccuracies, results demonstrate CDDM’s potential to enhance model selection, streamline AutoML workflows, and reduce computational costs.
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Bots in web survey interviews: A showcase. Höhne, J. K., Claaßen, J., Shahania, S., & Broneske, D. (2025, März/April). Bots in web survey interviews: A showcase. Vortrag im Rahmen der General Online Research (GOR) Conference, Berlin.
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Bots in web surveys: Predicting robotic language in open narrative answers. Claaßen, J., Höhne, J. K., Bach, R., & Haensch, A.-C. (2025, März/April). Bots in web surveys: Predicting robotic language in open narrative answers. Vortrag im Rahmen der General Online Research (GOR) Conference, Berlin.
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Static and dynamic contextual embedding for AutoML in text classification tasks. Safikhani, P. (2025, März). Static and dynamic contextual embedding for AutoML in text classification tasks. Vortrag auf der Konferenz International Conference on Natural Language Processing (ICNLP 2025), Guangzhou, China.
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Jenseits von Anti-Elitismus und Kulturkämpfen: Wie vermeintliche Sorgen der Bürger:innen den Populismus der AfD auf TikTok prägen. Meyer, H., Niemann-Lenz, J., Rodeck, L., & Revers, M. (2025, März). Jenseits von Anti-Elitismus und Kulturkämpfen: Wie vermeintliche Sorgen der Bürger:innen den Populismus der AfD auf TikTok prägen. Vortrag auf dem Workshop DGPuK Pre-Conference: Erosion demokratischer Werte?, Berlin, Deutschland.
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VerbCraft: Morphologically-aware Armenian text generation using LLMs in low-resource settings. Avetisyan, H. (2025, März). VerbCraft: Morphologically-aware Armenian text generation using LLMs in low-resource settings. Poster auf der Konferenz The Joint 25th Nordic Conference on Computational Linguistics and 11th Baltic Conference on Human Language Technologies (NoDaLiDa/Baltic-HLT 2025), Tallinn, Estonia.
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How does smartphone participation in probability-based web surveys differ across Europe? Höhne, J. K., Claaßen, J., Gummer, T., & Rettig, T. (2025, Februar). How does smartphone participation in probability-based web surveys differ across Europe? Vortrag im Rahmen der 7th Conference of Current Innovations in Probability-based Household Internet Panel Research (CIPHER), Washington, D.C., USA.
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Synthesizing LGBTQ-related attitudes: Comparing LLM-driven bots and survey respondents. Claaßen, J., & Höhne, J. K. (2025, Februar). Synthesizing LGBTQ-related attitudes: Comparing LLM-driven bots and survey respondents. Poster auf dem Workshop Novel Data Sources, New Methods, and Computational Approaches for Understanding Discrimination and Bias, MZES, Mannheim.
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NLP beyond boundaries: Integrating high-resource foundations with low-resource innovations. Avetisyan, H. (2025, Februar). NLP beyond boundaries: Integrating high-resource foundations with low-resource innovations. Vortrag auf dem Kolloquium Doktorandentag at Otto von Guericke University Magdeburg, Faculty of Computer Science, Magdeburg, Germany.
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Enhancing AutoML for NLP: Context-aware hyperparameter tuning and text representation using large language models. Safikhani, P. (2025, Februar). Enhancing AutoML for NLP: Context-aware hyperparameter tuning and text representation using large language models. Vortrag auf dem Kolloquium Doktorandentag at Otto von Guericke University Magdeburg, Faculty of Computer Science, Magdeburg, Germany.
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Tell me more! Using multiple features for binary text classification with a Zero-Shot model. Broneske, D., Italya, N., & Mierisch, F. (2024, Dezember). Tell me more! Using multiple features for binary text classification with a Zero-Shot model. Vortrag auf der Konferenz 23rd International Conference on Machine Learning and Applications (ICMLA 2024), Miami, Florida, USA.
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Publishing fine-grained DDI metadata: Learnings from efforts in three different RDCs. Wenzig, K., Daniel, A., Hansen, D., Coberg, T., & Tudose, M. (2024, Dezember). Publishing fine-grained DDI metadata: Learnings from efforts in three different RDCs. Vortrag auf der Konferenz 16th European DDI Users Conference (EDDI 2024), Chur, Schweiz.
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Die Forschungsdateninfrastruktur HEADS: Potenziale und Möglichkeiten. Gottburgsen, A., & Laajouzi, R. (2024, November). Die Forschungsdateninfrastruktur HEADS: Potenziale und Möglichkeiten. Vortrag in der Sitzung des Nutzerbeirats des Deutschen Zentrums für Hochschul- und Wissenschaftsforschung am 21.11.2024, online.
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