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
As part of the reorganization of the research database, the previous lists of research projects are no longer available. Die Zukunft geht in Richtung Volltextsuche und Filterung, um bestmögliche Suchergebnisse für unsere Besucher:innen zur Verfügung zu stellen.
In the meantime, you can easily find the projects via text search using the following link: «To the new search in the project database»
Publications
-
Ulasik, Malgorzata Anna; Hürlimann, Manuela; Dubel, Bogumila; Kaufmann, Yves; Rudolf, Silas; Deriu, Jan Milan; Mlynchyk, Katsiaryna; Hutter, Hans-Peter; Cieliebak, Mark,
2021.
ZHAW-CAI : ensemble method for Swiss German speech to Standard German text [paper].
In:
Benites de Azevedo e Souza, Fernando; Tuggener, Don; Hürlimann, Manuela; Cieliebak, Mark; Vogel, Manfred, eds.,
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-23889
-
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
-
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
-
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
-
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