Projects

Emotion analysis has typically been formulated as text classification task in which predefined emotion labels are assigned to textual units. The label set commonly follows the set of basic emotions as proposed by Ekman (Anger, Fear, Joy, Surprise, Sadness, Disgust) or Plutchik (adding Trust and Anticipation) or the valence-arousal-dominance model. This constitutes a gap between the state of research in psychology and computational linguistics, as the appraisal theories are widely accepted, but have not been used so far for emotion analysis in text. With CEAT, we fill this gap and develop computational models of the cognitive appraisal of events and, to a lesser degree, of bodily symptoms and action tendencies. To represent the cognitive appraisal, we build on top of Smith/Ellsworth’s (1985) work who show that the variables pleasantness, responsibility, certainty, attention, effort and situational control are sufficient to discriminate between a set of 15 emotions. In this project, we create two approaches to assign these appraisal dimensions to textual event descriptions, firstly by building on top of semantic parsing and secondly in a deep learning setting. Based on these dimensions, we then predict the emotion associated with the textual fragment. This will lead to models that can automatically assign an emotion to an event description, even if no emotion words or self reports of feeling are available.

• Role: Principle Investigator
• Other Project Members: N/A
• Funding: DFG

Most research on methods and models for automatic fact checking, which can distinguish misinformation and desinformation from correct information, focus on the news domain. News, including those shared in social media spaces, are checked for their truthfulness.Such methods have not been developed for the biomedical domain yet. Challenges include the richness of (established) sources of information, the complexity of information, as well as the differences between the language of experts and medical laypeople.In this project, we develop information extraction systems for laypeople and expert language, map the extracted information onto each other and finally check their truthfulness, based on established sources.The project combines therefore methods from transfer learning, information extraction, and fact checking for the biomedical domain, especially in social media.

• Role: Principle Investigator
• Other Project Members: Laura Bostan
• Funding: DFG

• Role: Co-Principle Investigator
• PIs: Kai Sassenberg (Leibniz-Institut für Wissensmedien) and Sebastian Padó (Uni Stuttgart)
• Other Project Members: Enrica Troiano (Ph.D. student)
• Funding: University of Stuttgart

The goal of this project is to develop a better understanding of the impact of emotion on the perception of facts. This aspect is for instance relevant in the distribution of fake news. We further develop methods which are able to separate factual content of statements from the emotional connotation.

More information:

• Paper at ACL 2019 by Troiano, Pado, Klinger: Crowdsourcing and Validating Event-focused Emotion Corpora for German and English
• German article in the Wissensdurst Magazin
• Press release

• Role: Co-Principle Investigator and Proposal Author
• Principle Investigator: Philipp Cimiano and Hans-Werner Mueller
• Other Project Members: Nicole Brazda (Co-PI), Hendrik ter Horst, Veronica Estrada, Jessica Schira, Christian Ohmann
• Former Project Members: Matthias Hartung
• Funding: BMBF, i:DSem

We develop automatic information extraction methods to populate a database of preclinical experiments in the domain of spinal cord injuries.

Partners are:

• Semantic Computing Group, University of Bielefeld
• Coordination Center for Clinical Studies Düsseldorf
• Molecular Neurobiology at University Hospital Düsseldorf with involvement of the Center for Neuronal Regeneration

More information:

• PSINK Website

The Chair of Theoretical Computational Linguistics is a partner in this BMBF-funded project in which we work on emotion analysis for literature analysis. Partners at University of Stuttgart are: Institut für Literaturwissenschaft / Germanistische Mediävistik, Institut für Visualisierung und Interaktive Systeme, Historisches Institut / Landesgeschichte, Institut für Sozialwissenschaften / Internationale Beziehungen und Europäische Integration, Institut für Maschinelle Sprachverarbeitung, Institut für Philosophie / Wissenschaftstheorie und Technikphilosophie, Institut für Literaturwissenschaft / Neuere Deutsche Literatur, Institut für Linguistik / Romanistik, Institut für Literaturwissenschaft / Digital Humanities, Stuttgart Research Centre for Text Studies

More information, including publications:

• CRETA Website

• Role: Principle Investigator
• Other project members: Camilo Thorne (Postdoc)
• Funding: MWK Baden-Württemberg and University of Stuttgart

In the Life Sciences, most information is only available in free text in scientific publications. Automatic methods to extract such knowledge and to provide it in structured databases is challenged by a dilemma: Especially if potentially new information is detected in text, it is unclear if this information is actually correct or if it is wrongly extracted, for instance because the text is formulated in an uncommon way. In this project, methods will be developed which help to estimate the reliability of extracted information from biomedical publications.

• Role: Principle Investigator
• Other project members: Christian Scheible, Hanna Kicherer
• Funding: Chefkoch GmbH

• Role: Postdoc in Project MMI
• Funding: German Federal Ministry of Research and Education (BMBF)

• Role: Principle Investigator
• Funding: Online Akademie GmbH

• Role: Postdoc, (project lead at Fraunhofer SCAI, taken over from Christoph Friedrich)
• Funding: European Commission
• Other partners: IBM Reseach HAIFA, Isreal, Institute of Communication and Computer Systems, Greece, Fraunhofer SCAI, Germany, University of Essec, UK, ATOS Origin, Spain, K.U. Leuven, Belgium, Athens Technology Center, Greece, Hellenic Parliament, Greece

• Role: Ph.D. student
• Funding: European Commission
• List of Partners

• Role: Ph.D. student
• Funding: Fraunhofer and Max-Planck Societies

• Other partners: MPG-FhG Collaboration Project with Fraunhofer FIRST, SCAI, IAIS, ITWM and Max-Planck Friedrich Miescher Laboratory, Institute for biological Cybernetics, Institute for Informatics, Institute for Molecular Genetics (information at Fraunhofer ITWM)

• Learning and Inference Platform