63 citations · 345 across the 53 of their papers we have counts for
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Experiencer-Specific Emotion and Appraisal Prediction
Maximilian Wegge, Enrica Troiano, Laura Oberländer +1
Emotion classification in NLP assigns emotions to texts, such as sentences or paragraphs. With texts like "I felt guilty when he cried", focusing on the sentence level disregards t…
Entity-based Claim Representation Improves Fact-Checking of Medical Content in Tweets
Amelie Wührl, Roman Klinger
False medical information on social media poses harm to people's health. While the need for biomedical fact-checking has been recognized in recent years, user-generated medical con…
Natural Language Inference Prompts for Zero-shot Emotion Classification in Text across Corpora
Flor Miriam Plaza-del-Arco, María-Teresa Martín-Valdivia, Roman Klinger
Within textual emotion classification, the set of relevant labels depends on the domain and application scenario and might not be known at the time of model development. This confl…
Dimensional Modeling of Emotions in Text with Appraisal Theories: Corpus Creation, Annotation Reliability, and Prediction
Enrica Troiano, Laura Oberländer, Roman Klinger
The most prominent tasks in emotion analysis are to assign emotions to texts and to understand how emotions manifest in language. An observation for NLP is that emotions can be com…
CoVERT: A Corpus of Fact-checked Biomedical COVID-19 Tweets
Isabelle Mohr, Amelie Wührl, Roman Klinger
Over the course of the COVID-19 pandemic, large volumes of biomedical information concerning this new disease have been published on social media. Some of this information can pose…
Recovering Patient Journeys: A Corpus of Biomedical Entities and Relations on Twitter (BEAR)
Amelie Wührl, Roman Klinger
Text mining and information extraction for the medical domain has focused on scientific text generated by researchers. However, their direct access to individual patient experience…
x-enVENT: A Corpus of Event Descriptions with Experiencer-specific Emotion and Appraisal Annotations
Enrica Troiano, Laura Oberländer, Maximilian Wegge +1
Emotion classification is often formulated as the task to categorize texts into a predefined set of emotion classes. So far, this task has been the recognition of the emotion of wr…
"splink" is happy and "phrouth" is scary: Emotion Intensity Analysis for Nonsense Words
Valentino Sabbatino, Enrica Troiano, Antje Schweitzer +1
People associate affective meanings to words - "death" is scary and sad while "party" is connotated with surprise and joy. This raises the question if the association is purely a p…
Items from Psychometric Tests as Training Data for Personality Profiling Models of Twitter Users
Anne Kreuter, Kai Sassenberg, Roman Klinger
Machine-learned models for author profiling in social media often rely on data acquired via self-reporting-based psychometric tests (questionnaires) filled out by social media user…
Embarrassingly Simple Performance Prediction for Abductive Natural Language Inference
Emīls Kadiķis, Vaibhav Srivastav, Roman Klinger
The task of abductive natural language inference (αnli), to decide which hypothesis is the more likely explanation for a set of observations, is a particularly difficult type of NL…
On the Complementarity of Images and Text for the Expression of Emotions in Social Media
Anna Khlyzova, Carina Silberer, Roman Klinger
Authors of posts in social media communicate their emotions and what causes them with text and images. While there is work on emotion and stimulus detection for each modality separ…