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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von Däniken, Pius; Cieliebak, Mark,
2017.
Transfer learning and sentence level features for named entity recognition on tweets [paper].
In:
Proceedings of the 3rd Workshop on Noisy User-generated Text.
3rd Workshop on Noisy User-generated Text (W-NUT), Copenhagen, Denmark, 7 September 2017.
Association for Computational Linguistics.
pp. 166-171.
Available from: https://doi.org/10.21256/zhaw-1528
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Uzdilli, Fatih; Cieliebak, Mark; Egger, Dominic,
2016.
Adverse drug reaction detection using an adapted sentiment classifier [paper].
In:
Proceedings of the Social Media Mining Shared Task Workshop at the Pacific Symposium on Biocomputing.
Pacific Symposium on Biocomputing, Fairmont Orchid, USA, 4-8 January 2016.
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2016.
Deep learning for infinite applications in text analytics.
swiTT Report.
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Cieliebak, Mark; Keck Frei, Andrea,
2016.
Influence of flipped classroom on technical skills and non-technical competences of IT students [paper].
In:
Global Engineering Education Conference (EDUCON), Abu Dhabi, UAE, 10-13 April 2016.
IEEE.
pp. 1012-1016.
Available from: https://doi.org/10.1109/EDUCON.2016.7474676
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2016.
Optimising Deep Learning for Infinite Applications in Text Analytics.
ERCIM News.
107.
Available from: http://dreamboxx.com/mark/data/ercim_news_107_2016_article.pdf