TY  - CHAP
A1  - Kolditz, Melanie
A1  - Albin, Thivaharan
A1  - Albracht, Kirsten
A1  - Brüggemann, Gert-Peter
A1  - Abel, Dirk
T1  - Isokinematic leg extension training with an industrial robot
T2  - 6th IEEE RAS/EMBS International Conference on Biomedical Robotics and Biomechatronics (BioRob) June 26-29, 2016. UTown, Singapore
Y1  - 2016
U6  - http://dx.doi.org/10.1109/BIOROB.2016.7523750
SP  - 950
EP  - 955
ER  - 
TY  - CHAP
A1  - Kohl, Philipp
A1  - Schmidts, Oliver
A1  - Klöser, Lars
A1  - Werth, Henri
A1  - Kraft, Bodo
A1  - Zündorf, Albert
T1  - STAMP 4 NLP – an agile framework for rapid quality-driven NLP applications development
T2  - Quality of Information and Communications Technology. QUATIC 2021
N2  - The progress in natural language processing (NLP) research over the last years, offers novel business opportunities for companies, as automated user interaction or improved data analysis. Building sophisticated NLP applications requires dealing with modern machine learning (ML) technologies, which impedes enterprises from establishing successful NLP projects. Our experience in applied NLP research projects shows that the continuous integration of research prototypes in production-like environments with quality assurance builds trust in the software and shows convenience and usefulness regarding the business goal. We introduce STAMP 4 NLP as an iterative and incremental process model for developing NLP applications. With STAMP 4 NLP, we merge software engineering principles with best practices from data science. Instantiating our process model allows efficiently creating prototypes by utilizing templates, conventions, and implementations, enabling developers and data scientists to focus on the business goals. Due to our iterative-incremental approach, businesses can deploy an enhanced version of the prototype to their software environment after every iteration, maximizing potential business value and trust early and avoiding the cost of successful yet never deployed experiments.
KW  - Machine learning
KW  - Process model
KW  - Natural language processing
Y1  - 2021
SN  - 978-3-030-85346-4
SN  - 978-3-030-85347-1
U6  - http://dx.doi.org/10.1007/978-3-030-85347-1_12
N1  - International Conference on the Quality of Information and Communications Technology, QUATIC 2021, 8-11 September, Algarve, Portugal
SP  - 156
EP  - 166
PB  - Springer
CY  - Cham
ER  - 
TY  - CHAP
A1  - Kohl, Philipp
A1  - Freyer, Nils
A1  - Krämer, Yoka
A1  - Werth, Henri
A1  - Wolf, Steffen
A1  - Kraft, Bodo
A1  - Meinecke, Matthias
A1  - Zündorf, Albert
ED  - Conte, Donatello
ED  - Fred, Ana
ED  - Gusikhin, Oleg
ED  - Sansone, Carlo
T1  - ALE: a simulation-based active learning evaluation framework for the parameter-driven comparison of query strategies for NLP
T2  - Deep Learning Theory and Applications. DeLTA 2023. Communications in Computer and Information Science
N2  - Supervised machine learning and deep learning require a large amount of labeled data, which data scientists obtain in a manual, and time-consuming annotation process. To mitigate this challenge, Active Learning (AL) proposes promising data points to annotators they annotate next instead of a subsequent or random sample. This method is supposed to save annotation effort while maintaining model performance.

However, practitioners face many AL strategies for different tasks and need an empirical basis to choose between them. Surveys categorize AL strategies into taxonomies without performance indications. Presentations of novel AL strategies compare the performance to a small subset of strategies. Our contribution addresses the empirical basis by introducing a reproducible active learning evaluation (ALE) framework for the comparative evaluation of AL strategies in NLP.

The framework allows the implementation of AL strategies with low effort and a fair data-driven comparison through defining and tracking experiment parameters (e.g., initial dataset size, number of data points per query step, and the budget). ALE helps practitioners to make more informed decisions, and researchers can focus on developing new, effective AL strategies and deriving best practices for specific use cases. With best practices, practitioners can lower their annotation costs. We present a case study to illustrate how to use the framework.
KW  - Active learning
KW  - Query learning
KW  - Natural language processing
KW  - Deep learning
KW  - Reproducible research
Y1  - 2023
SN  - 978-3-031-39058-6 (Print)
SN  - 978-3-031-39059-3 (Online)
U6  - http://dx.doi.org/978-3-031-39059-3
N1  - 4th International Conference, DeLTA 2023, Rome, Italy, July 13–14, 2023.
SP  - 235
EP  - 253
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
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  - http://dx.doi.org/10.5220/0010559201480156
N1  - Proceedings of the 2nd International Conference on Deep Learning Theory and Applications, DeLTA2021, July 7-9, 2021
SP  - 148
EP  - 156
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  - DeLTA 2023: 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  - http://dx.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  - 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  - 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  - 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  - Kirchner, Patrick
A1  - Henkel, H.
A1  - Näther, Niko
A1  - Spelthahn, H.
A1  - Schneider, A.
A1  - Berger, J.
A1  - Kolstad, J.
A1  - Friedrich, P.
A1  - Schöning, Michael Josef
T1  - RFID-basiertes Sensorsystem zur Realisierung intelligenter Verpackungen für die Nahrungsmittelindustrie
T2  - KMU - innovativ: IKT 2008. CD-ROM : BMBF-Statustagung KMU - innovativ: IKT, Darmstadt, 17. - 18. Nov. 2008
Y1  - 2008
IS  - CD-ROM-Ausg.
PB  - BMBF
CY  - Berlin
ER  - 
TY  - CHAP
A1  - Kirchhof, Michael
A1  - Kraft, Bodo
T1  - Dogmatisches „Entweder agil oder klassisch" im Projektmanagement hat ausgedient - die richtige Mischung macht's
T2  - Projekt-Sternstunden : strahlende Erfolge durch Kompetenz
Y1  - 2011
SN  - 978-3-924841-60-7
N1  - PM-Forum <28, 2011, Nürnberg> ; 
PM-Forum 2011, 28. Internationales Deutsches Projektmanagement-Forum ; Nürnberg, 25. - 26.10.2011 ; Tagungsband
SP  - 414
EP  - 425
PB  - GPM
CY  - Nürnberg
ER  -