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
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Stadelmann, Thilo; Cieliebak, Mark; Stockinger, Kurt,
2015.
Toward automatic data curation for open data.
ERCIM News.
2015(100), pp. 32-33.
Available from: https://doi.org/10.21256/zhaw-3643
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2014.
Auswirkungen von Flipped Classroom auf Fachwissen und Kompetenzen von Studierenden.
ZHAW Zürcher Hochschule für Angewandte Wissenschaften.
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2014.
Flip your classroom : but be aware!.
Lifelong Learning in Europe.
2014(4).
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Dürr, Oliver; Uzdilli, Fatih; Cieliebak, Mark,
2014.
JOINT_FORCES : unite competing sentiment classifiers with random forest [paper].
In:
Proceedings of the International Workshop on Semantic Evaluation (SemEval-2014).
International Workshop on Semantic Evaluation (SemEval-2014), Dublin, Irland, 23-24 August 2014.
Association for Computational Linguistics.
pp. 366-369.
Available from: https://doi.org/10.21256/zhaw-3779
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Cieliebak, Mark; Dürr, Oliver; Uzdilli, Fatih,
2014.
Meta-classifiers easily improve commercial sentiment detection tools [paper].
In:
Proceedings of the 9th International Conference on Language Resources and Evaluation (LREC 2014).
9th International Conference on Language Resources and Evaluation, Reykjavik, Iceland, 26-31 May 2014.
Association for Computational Linguistics.
pp. 3943-3947.