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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Deriu, Jan Milan; Rodrigo, Alvaro; Otegi, Arantxa; Guillermo, Echegoyen; Rosset, Sophie; Agirre, Eneko; Cieliebak, Mark, eds.,
2019.
Survey on evaluation methods for dialogue.
ZHAW Zürcher Hochschule für Angewandte Wissenschaften.
Available from: https://doi.org/10.21256/zhaw-18985
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Meierhofer, Jürg; Stadelmann, Thilo; Cieliebak, Mark,
2019.
.
In:
Braschler, Martin; Stadelmann, Thilo; Stockinger, Kurt, eds.,
Applied data science : lessons learned for the data-driven business.
Cham:
Springer.
pp. 47-61.
Available from: https://doi.org/10.1007/978-3-030-11821-1_4
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Amirian, Mohammadreza; Rombach, Katharina; Tuggener, Lukas; Schilling, Frank-Peter; Stadelmann, Thilo,
2019.
Efficient deep CNNs for cross-modal automated computer vision under time and space constraints [paper].
In:
ECML-PKDD 2019, Würzburg, Germany, 16-19 September 2019.
ZHAW Zürcher Hochschule für Angewandte Wissenschaften.
Available from: https://doi.org/10.21256/zhaw-18357
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Venzin, Valentin; Deriu, Jan Milan; Didier, Orel; Cieliebak, Mark,
2019.
Fact-aware abstractive text summarization using a pointer-generator network [paper].
In:
4th Swiss Text Analytics Conference (SwissText 2019), Winterthur, June 18-19 2019.
Swisstext.
Available from: https://doi.org/10.21256/zhaw-18988
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Aghaebrahimian, Ahmad; Cieliebak, Mark,
2019.
Hyperparameter tuning for deep learning in natural language processing [paper].
In:
4th Swiss Text Analytics Conference (SwissText 2019), Winterthur, June 18-19 2019.
Swisstext.
Available from: https://doi.org/10.21256/zhaw-18993