Description
Particular focus is given on Natural Language Processing with emphasis on efficient and resource lean methods for language modeling (e.g., predictive keyboards), text generation and classification (e.g, from radiograph to diagnostic text), as well as information extraction (e.g., toxic spans detection). Particular emphasis is given on language technology applied on medical texts, to extract accurate and relevant information from very large clinical text sets. The latter is performed in close collaboration with the clinical text mining group.
Latest publications
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Automotive fault nowcasting with machine learning and natural language processing
Machine Learning, 2023 |
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Customized Neural Predictive Medical Text: A Use-Case on Caregivers
In Artificial Intelligence in Medicine (AIME), 2021 |
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Clinical Predictive Keyboard using Statistical and Neural Language
Modeling
In International Symposium on Computer-Based Medical Systems (CBMS), 2020 |
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Medical Image Tagging by Deep Learning and Retrieval
In International Conference of the Cross-Language Evaluation Forum for European Languages, 2020 |
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A Survey on Biomedical Image Captioning
arXiv preprint arXiv:1905.13302, 2019 |
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Convai at semeval-2019 task 6: Offensive language identification and categorization with perspective and bert
In Proceedings of the 13th International Workshop on Semantic Evaluation, 2019 |
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A Survey on Biomedical Image Captioning
In Proceedings of the Second Workshop on Shortcomings in Vision and Language, 2019 |
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John Pavlopoulos
machine learning for natural language processing, applications to healthcare and education
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