We present our submission to Task 11, Bridging the Gap in Text-Based Emotion Detection, of the 19th International Workshop on Semantic Evaluation (SemEval) 2025. We participated in track A, multi-label emotion detection, in both German and English. Our approach is based on fine-tuning transformer models for each language, and our models achieve a Macro F1 of 0.75 and 0.62 for English and German respectively. Furthermore, we analyze the data available for training to gain insight into the model predictions.

We present our results for the shared tasks 2, 4 and 9 at the SMM4H Workshop at COLING 2022 achieved by succesfully fine-tuning pre-trained language models to the downstream tasks. We identify the occurence of code-switching in the test data for task 2 as a possible source of considerable performance degradation on the test set scores. We successfully exploit structural linguistic similarities in the datasets of tasks 4 and 9 for training on joined datasets, scoring first in task 9 and on par with SOTA in task 4.

We present our submission to Task 2 of the Socio-political and Crisis Events Detection Shared Task at the CASE @ ACL-IJCNLP 2021 workshop. The task at hand aims at the fine-grained classification of socio-political events. Our best model was a fine-tuned RoBERTa transformer model using document embeddings. The corpus consisted of a balanced selection of sub-events extracted from the ACLED event dataset. We achieved a macro F-score of 0.923 and a micro F-score of 0.932 during our preliminary experiments on a held-out test set. The same model also performed best on the shared task test data (weighted F-score = 0.83). To analyze the results we calculated the topic compactness of the commonly misclassified events and conducted an error analysis.

This paper describes our system submission for the ACL 2018 shared task on named entity recognition (NER) in code-switched Twitter data. Our best result (F1 = 53.65) was obtained using a Support Vector Machine (SVM) with 14 features combined with rule-based post processing.