TY - CHAP A1 - Klein, Stefan A1 - Lindemann, Markus ED - Vogel, Douglas R. T1 - New architectures for web-enabled EDI-applications and their impact on VANS T2 - Global business in practice : proceedings of the Tenth International Bled Electronic Commerce Conference BLED '97, Bled, Slovenia, June 9-11 1997 Y1 - 1997 SP - 556 EP - 573 PB - Moderna organizacija CY - Kranj ER - TY - CHAP A1 - Kleine, Harald A1 - Kallweit, Stephan A1 - Michaux, Frank A1 - Havermann, Marc A1 - Olivier, Herbert T1 - PIV Measurement of Shock Wave Diffraction T2 - 18th International Symposium on Applications of Laser Techniques to Fluid Mechanics, 2016, Lissabon Y1 - 2016 SP - 1 EP - 14 ER - TY - CHAP A1 - Klocke, Martina T1 - Projektmodul im Bachelorstudiengang Maschinenbau und Mechatronik T2 - VDI-Workshop Projektorientiertes und problem-basiertes Lernen (PBL) in der Ingenieurausbildung Y1 - 2012 N1 - 25 Folien zum eingeladenen Vortrag beim VDI-Workshop. Darmstadt, 22./23.11.2012 SP - 1 EP - 25 ER - TY - CHAP A1 - Klocke, Martina T1 - Projektmodul im Bachelorstudiengang Maschinenbau bzw. Mechatronik T2 - Hochschulrektorenkonferenz HRK nexus : Kompetenzorientiertes Prüfen in den Ingenieurwissenschaften und in der Informatik. Gemeinsame Tagung von 4ING und nexus. 29.3.2011 Bremen Y1 - 2011 SP - 1 EP - 25 ER - TY - CHAP A1 - Kloock, Joachim P. A1 - Moreno, Lia A1 - Huachupoma, S. A1 - Xu, J. A1 - Wagner, Torsten A1 - Bratov, A. A1 - Doll, T. A1 - Vlasov, Y. A1 - Schöning, Michael Josef ED - Gerlach, Gerald T1 - Halbleiterbasierte Schwermetallsensorik auf der Basis von Chalkogenidgläsern für zukünftige „Lab on Chip“-Anwendungen T2 - 7. Dresdner Sensor-Symposium - Neue Herausforderungen und Anwendungen in der Sensortechnik Y1 - 2005 SN - 3-938863-29-3 SP - 221 EP - 224 PB - TUDpress, Verl. der Wissenschaften CY - Dresden ER - TY - CHAP A1 - Kloock, Joachim P. A1 - Schubert, J. A1 - Ermelenko, Y. A1 - Vlasov, Y. G. A1 - Bratov, A. A1 - Schöning, Michael Josef T1 - Thin-film sensors with chalcogenide glass materials – a general survey T2 - Biochemical sensing utilisation of micro- and nanotechnologies : Warsaw, [23rd - 26th] November 2005 / ed. by M. Mascini ... Y1 - 2006 SP - 92 EP - 97 CY - Warsaw ER - TY - CHAP A1 - Kloock, Joachim P. A1 - Schöning, Michael Josef T1 - Heavy metal detection with semiconductor devices based on PLD-prepared chalcogenide glass thin films T2 - Armenian Journal of Physics Y1 - 2007 SN - 1829-1171 SP - 95 EP - 98 ER - TY - CHAP A1 - Klöcker, Alexander T1 - Das Leitungsbauunternehmen - unternehmerische Strategie als Zukunftssicherung N2 - In: Alfha.net / Sektion Bauingenieurwesen: 1. [Erster] Erfahrungsaustausch : Absolventen des Fachbereichs Bauingenieurwesens berichten. 13. Oktober 2006. S. 21-22 Zusammenfassung des Vortrags KW - Leitungsbau KW - Leitungsbauunternehmen Y1 - 2006 ER - TY - CHAP A1 - Klöser, Lars A1 - Büsgen, André A1 - Kohl, Philipp A1 - Kraft, Bodo A1 - Zündorf, Albert ED - Conte, Donatello ED - Fred, Ana ED - Gusikhin, Oleg ED - Sansone, Carlo T1 - Explaining relation classification models with semantic extents T2 - Deep Learning Theory and Applications N2 - In recent years, the development of large pretrained language models, such as BERT and GPT, significantly improved information extraction systems on various tasks, including relation classification. State-of-the-art systems are highly accurate on scientific benchmarks. A lack of explainability is currently a complicating factor in many real-world applications. Comprehensible systems are necessary to prevent biased, counterintuitive, or harmful decisions. We introduce semantic extents, a concept to analyze decision patterns for the relation classification task. Semantic extents are the most influential parts of texts concerning classification decisions. Our definition allows similar procedures to determine semantic extents for humans and models. We provide an annotation tool and a software framework to determine semantic extents for humans and models conveniently and reproducibly. Comparing both reveals that models tend to learn shortcut patterns from data. These patterns are hard to detect with current interpretability methods, such as input reductions. Our approach can help detect and eliminate spurious decision patterns during model development. Semantic extents can increase the reliability and security of natural language processing systems. Semantic extents are an essential step in enabling applications in critical areas like healthcare or finance. Moreover, our work opens new research directions for developing methods to explain deep learning models. KW - Relation classification KW - Natural language processing KW - Natural language understanding KW - Information extraction KW - Trustworthy artificial intelligence Y1 - 2023 SN - 978-3-031-39058-6 (Print) SN - 978-3-031-39059-3 (Online) U6 - https://doi.org/10.1007/978-3-031-39059-3_13 N1 - 4th International Conference, DeLTA 2023, Rome, Italy, July 13–14, 2023. SP - 189 EP - 208 PB - Springer CY - Cham ER - TY - CHAP A1 - Klöser, Lars A1 - Kohl, Philipp A1 - Kraft, Bodo A1 - Zündorf, Albert T1 - Multi-attribute relation extraction (MARE): simplifying the application of relation extraction T2 - Proceedings of the 2nd International Conference on Deep Learning Theory and Applications DeLTA - Volume 1 N2 - Natural language understanding’s relation extraction makes innovative and encouraging novel business concepts possible and facilitates new digitilized decision-making processes. Current approaches allow the extraction of relations with a fixed number of entities as attributes. Extracting relations with an arbitrary amount of attributes requires complex systems and costly relation-trigger annotations to assist these systems. We introduce multi-attribute relation extraction (MARE) as an assumption-less problem formulation with two approaches, facilitating an explicit mapping from business use cases to the data annotations. Avoiding elaborated annotation constraints simplifies the application of relation extraction approaches. The evaluation compares our models to current state-of-the-art event extraction and binary relation extraction methods. Our approaches show improvement compared to these on the extraction of general multi-attribute relations. Y1 - 2021 SN - 978-989-758-526-5 U6 - https://doi.org/10.5220/0010559201480156 N1 - 2nd International Conference on Deep Learning Theory and Applications, DeLTA2021, July 7-9, 2021 SP - 148 EP - 156 PB - SciTePress CY - Setúbal ER -