Enhancing Crisis-Related Tweet Classification with Entity-Masked Language Modeling and Multi-Task Learning

  • 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.

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Metadaten
Author:Philipp Seeberger, Korbinian Riedhammer
DOI:https://doi.org/10.48550/arXiv.2211.11468
ArXiv Id:http://arxiv.org/abs/arXiv:2211.11468
Publisher:Association for Computational Linguistics
Document Type:conference proceeding (article)
Language:English
Date of first Publication:2022/11/21
Reviewed:Begutachtet/Reviewed
Release Date:2024/07/08
Pagenumber:9
First Page:70
Last Page:78
Konferenzangabe:2nd Workshop on NLP for Positive Impact
institutes:Fakultät Informatik
Licence (German):Keine Lizenz - Deutsches Urheberrecht gilt
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