Philipp Seeberger


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Information Type Classification with Contrastive Task-Specialized Sentence Encoders
Philipp Seeberger | Tobias Bocklet | Korbinian Riedhammer
Proceedings of the 19th Conference on Natural Language Processing (KONVENS 2023)


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Enhancing Crisis-Related Tweet Classification with Entity-Masked Language Modeling and Multi-Task Learning
Philipp Seeberger | Korbinian Riedhammer
Proceedings of the Second Workshop on NLP for Positive Impact (NLP4PI)

Social media has become an important information source for crisis management and provides quick access to ongoing developments and critical information. However, classification models suffer from event-related biases and highly imbalanced label distributions which still poses a challenging task. To address these challenges, we propose a combination of entity-masked language modeling and hierarchical multi-label classification as a multi-task learning problem. We evaluate our method on tweets from the TREC-IS dataset and show an absolute performance gain w.r.t. F1-score of up to 10% for actionable information types. Moreover, we found that entity-masking reduces the effect of overfitting to in-domain events and enables improvements in cross-event generalization.