Papers by Christian Biemann
Language over Labels: Contrastive Language Supervision Exceeds Purely Label-Supervised Classification Performance on Chest X-Rays (2022.aacl-srw)
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| Challenge: | Pretrained CLIP models lack domain-specific knowledge of text and images. |
| Approach: | They adapt CLIP-based models to the chest radiography domain using contrastive language supervision and a detailed ablation study of the batch and dataset size. |
| Outcome: | The proposed model outperforms supervised learning on labels on the MIMIC-CXR dataset while generalizing to the CheXpert and RSNA Pneumonia datasets. |
SCoT: Sense Clustering over Time: a tool for the analysis of lexical change (2021.eacl-demos)
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| Challenge: | Sense Clustering over Time (SCoT) is a network-based tool for analysing lexical change . it visualises word formation, change, and demise as clusters of similar words . SCoT has been successfully used in a European study on the changing meaning of ‘crisis’. |
| Approach: | They propose a new network-based tool for analysing lexical change using a dynamic network of word similarities. |
| Outcome: | The proposed tool has been successfully used in a European study on the changing meaning of ‘crisis’. |
Forum 4.0: An Open-Source User Comment Analysis Framework (2021.eacl-demos)
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Marlo Haering, Jakob Smedegaard Andersen, Chris Biemann, Wiebke Loosen, Benjamin Milde, Tim Pietz, Christian Stöcker, Gregor Wiedemann, Olaf Zukunft, Walid Maalej
| Challenge: | Using Forum 4.0, we analyze, aggregate, and visualize user comments based on labels defined by domain experts. |
| Approach: | They introduce an open-source framework to semi-automatically analyze, aggregate, and visualize user comments based on labels defined by domain experts. |
| Outcome: | The proposed framework can analyze, aggregate, and visualize user comments based on labels defined by domain experts. |