Natural Language Processing Group
“We combine foundational research with industrial applications to build new and innovative products and services, while at the same time exploring the necessary ethical and social boundaries.”
Fields of expertise
- Text analytics
- Dialogue systems
- Speech processing
The NLP research team develops technologies for the analysis, understanding and generation of speech and text. We combine methods from linguistics, natural language processing (NLP) and artificial intelligence to enable natural language communication between humans and machines. In our research, we work on topics such as text classification (e.g. sentiment analysis), chatbots/dialogue systems, text summarization, speech-to-text, speaker diarization and natural language generation. The group particularly focuses on Swiss German speech and text processing.
Services
- Insight: keynotes, trainings
- AI consultancy: workshops, expert support, advice, technology assessment
- Research and development: small to large-scale collaborative projects, third party-funded research, student projects, commercially applicable prototypes
Team
Head of Research Group
Projects
Unfortunately, no list of projects can be displayed here at the moment. Until the list is available again, the project search on the ZHAW homepage can be used.
Publications
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Benites, Fernando; Tuggener, Don; Hürlimann, Manuela; Cieliebak, Mark; Vogel, Manfred, eds.,
2021.
Proceedings of the Swiss Text Analytics Conference 2021.
6th Swiss Text Analytics Conference – SwissText 2021, Online, 14-16 June 2021.
CEUR Workshop Proceedings.
.
Available from: http://ceur-ws.org/Vol-2957/
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Tuggener, Don; Aghaebrahimian, Ahmad,
2021.
The Sentence End and Punctuation Prediction in NLG text (SEPP-NLG) shared task 2021 [paper].
In:
Proceedings of the Swiss Text Analytics Conference 2021.
Swiss Text Analytics Conference – SwissText 2021, Online, 14-16 June 2021.
CEUR Workshop Proceedings.
Available from: https://doi.org/10.21256/zhaw-23258
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Amirian, Mohammadreza; Tuggener, Lukas; Chavarriaga, Ricardo; Satyawan, Yvan Putra; Schilling, Frank-Peter; Schwenker, Friedhelm; Stadelmann, Thilo,
2021.
Two to trust : AutoML for safe modelling and interpretable deep learning for robustness [paper].
In:
Postproceedings of the 1st TAILOR Workshop on Trustworthy AI at ECAI 2020.
1st TAILOR Workshop on Trustworthy AI at ECAI 2020, Santiago de Compostela, Spain, 29-30 August 2020.
Springer.
Available from: https://doi.org/10.21256/zhaw-22061
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2021.
Improving a semantic parser through user interaction.
Winterthur:
ZHAW Zürcher Hochschule für Angewandte Wissenschaften.
Available from: https://doi.org/10.21256/zhaw-22938
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Tuggener, Lukas; Satyawan, Yvan Putra; Pacha, Alexander; Schmidhuber, Jürgen; Stadelmann, Thilo,
2021.
The DeepScoresV2 dataset and benchmark for music object detection [paper].
In:
2020 25th International Conference on Pattern Recognition (ICPR).
25th International Conference on Pattern Recognition 2020 (ICPR’20), Online, 10-15 January 2021.
IEEE.
pp. 9188-9195.
Available from: https://doi.org/10.1109/ICPR48806.2021.9412290