Natural Language Processing Group
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“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
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- 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
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NLP Community Building - ComBi
SwissNLP would like to take concerted action to better network Swiss players from industry, science and administration in the field of Natural Language Processing (NLP). For this reason various activities are to be carried out until the end of 2025 such as expert group meetings, applied conferences, data ...
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AI4CP: AI for self-organizing Content Platform
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Towards a Voice-Based Chatbot for Language Learners (ChaLL)
We take first steps towards developing ChaLL, a voice-based chatbot that provides language learners with opportunities to practice speaking in both focused and unfocused task-based conversations and receive feedback, free from the time constraints and pressures of the traditional classroom setting. ...
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PRISM: Predicting Radicalization Events in Social Media User Timelines
The PRISM project focuses on detecting radicalization events in Social Media networks. Overall, we are interested in unveiling the mechanics that lead to the event of extremist ideology being transferred and incorporated into a social media user’s world view. Specifically, the proposed project aims to identify ...
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DOSSMA – Detection of Suspicious Social Media Activities
The DOSSMA project will investigate suspicious and malicious behaviour on social media platforms. In a first phase, we will compile an extensive survey report on the areas that are currently being researched, including the respective state-of-the-art, existing solutions and initiatives. This report will serve as a ...
Publications
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von Grünigen, Dirk; Weilenmann, Martin; Deriu, Jan Milan; Cieliebak, Mark,
2017.
Potential and limitations of cross-domain sentiment classification [paper].
In:
Proceedings of the Fifth International Workshop on Natural Language Processing for Social Media.
Fifth International Workshop on Natural Language Processing for Social Media, Valencia, Spain, 3-7 April 2017.
Stroudsburg:
Association for Computational Linguistics.
pp. 17-24.
Available from: https://doi.org/10.18653/v1/W17-1103
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Falkner, Nicole; Dolce, Stefano; von Däniken, Pius; Cieliebak, Mark,
2017.
Swiss chocolate at CAp 2017 NER challenge : partially annotated data and transfer learning [paper].
In:
19th Conference sur l'Apprentissage Automatique, Grenoble, 28-30 June 2017.
ZHAW Zürcher Hochschule für Angewandte Wissenschaften.
Available from: https://doi.org/10.21256/zhaw-1532
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Deriu, Jan Milan; Cieliebak, Mark,
2017.
In:
Proceedings of the 11th International Workshop on Semantic Evaluation (SemEval-2017).
SemEval 2017 - International Workshop on Semantic Evaluation, Vancouver, Canada, 3-4 August 2017.
Association for Computational Linguistics.
pp. 334-338.
Available from: https://doi.org/10.18653/v1/S17-2054
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Müller, Simon; Huonder, Tobias; Deriu, Jan Milan; Cieliebak, Mark,
2017.
In:
Proceedings of the 11th International Workshop on Semantic Evaluation (SemEval-2017).
11th International Workshop on Semantic Evaluation, Vancouver, Canada, 3-4 August 2017.
Association for Computational Linguistics.
pp. 766-771.
Available from: https://doi.org/10.21256/zhaw-1529
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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