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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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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Deriu, Jan Milan; Mlynchyk, Katsiaryna; Schläpfer, Philippe; Rodrigo, Alvaro; von Grünigen, Dirk; Kaiser, Nicolas; Stockinger, Kurt; Agirre, Eneko; Cieliebak, Mark,
2020.
A methodology for creating question answering corpora using inverse data annotation[paper].
In:
Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics.
58th Annual Meeting of the Association for Computational Linguistics (ACL 2020), online, 5-10 July 2020.
Association for Computational Linguistics.
pp. 897-911.
Available from: https://doi.org/10.18653/v1/2020.acl-main.84
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Ebling, Sarah; Tuggener, Don; Hürlimann, Manuela; Cieliebak, Mark; Volk, Martin, eds.,
2020.
5th SwissText & 16th KONVENS Joint Conference, Zurich (online), 24-25 June 2020.
.
Available from: http://ceur-ws.org/Vol-2624/
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von Däniken, Pius; Hürlimann, Manuela; Cieliebak, Mark,
2020.
Overview of the GermEval 2020 shared task on Swiss German language identification[paper].
In:
Ebling, Sarah; Tuggener, Don; Hürlimann, Manuela; Cieliebak, Mark; Volk, Martin, eds.,
Proceedings of the 5th Swiss Text Analytics Conference (SwissText) & 16th Conference on Natural Language Processing (KONVENS).
5th SwissText & 16th KONVENS Joint Conference, Zurich (online), 24-25 June 2020.
CEUR Workshop Proceedings.
Available from: https://doi.org/10.21256/zhaw-21549