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
Projects
- Virtual Kids
- Interscriber – Turning Dialogues into Actionable Insights
- SwissText 2021
- Speech-to-Text for Swiss German
- AuSuM – Automatic Supply Chain Monitoring
Team
Head of Research Group
Publications
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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:
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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Benites de Azevedo e Souza, Fernando; Hürlimann, Manuela; von Däniken, Pius; Cieliebak, Mark,
2020.
ZHAW-InIT : social media geolocation at VarDial 2020 [paper].
In:
Zampieri, Marcos; Nakov, Preslav; Ljubešić, Nikola; Tiedemann, Jörg; Scherrer, Yves, eds.,
Proceedings of the 7th Workshop on NLP for Similar Languages, Varieties and Dialects.
Workshop on NLP for Similar Languages, Varieties and Dialects, Barcelona (Spain), online, 13 December 2020.
International Committee on Computational Linguistics (ICCL).
pp.254-264.
Available from: https://doi.org/10.21256/zhaw-21551
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Tuggener, Lukas; Amirian, Mohammadreza; Benites de Azevedo e Souza, Fernando; von Däniken, Pius; Gupta, Prakhar; Schilling, Frank-Peter; Stadelmann, Thilo,
2020.
Design patterns for resource-constrained automated deep-learning methods.
AI.
1(4),
pp.510-538.
Available from: https://doi.org/10.3390/ai1040031
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Deriu, Jan Milan; Tuggener, Don; von Däniken, Pius; Campos, Jon Ander; Rodrigo, Alvaro; Belkacem, Thiziri; Soroa, Aitor; Agirre, Eneko; Cieliebak, Mark,
2020.
In:
Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP).
Conference on Empirical Methods in Natural Language Processing (EMNLP), Online, 16-20 November 2020.
Association for Computational Linguistics.
pp.3971-3984.
Available from: https://doi.org/10.18653/v1/2020.emnlp-main.326
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Tuggener, Lukas; Satyawan, Yvan Putra; Pacha, Alexander; Schmidhuber, Jürgen; Stadelmann, Thilo,
2020.
The DeepScoresV2 dataset and benchmark for music object detection [paper].
In:
Proceedings of the 25th International Conference on Pattern Recognition 2020 (ICPR’20).
25th International Conference on Pattern Recognition 2020 (ICPR’20), Online, 10-15 January 2021.
IAPR.
Available from: https://doi.org/10.21256/zhaw-20647