Biomedical Argument Mining Based on Sequential Multi-Task Learning
| العنوان: | Biomedical Argument Mining Based on Sequential Multi-Task Learning |
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| المؤلفون: | Jiasheng Si, Liu Sun, Deyu Zhou, Jie Ren, Lin Li |
| المصدر: | IEEE/ACM Transactions on Computational Biology and Bioinformatics. 20:864-874 |
| بيانات النشر: | Institute of Electrical and Electronics Engineers (IEEE), 2023. |
| سنة النشر: | 2023 |
| مصطلحات موضوعية: | Applied Mathematics, Genetics, Biotechnology |
| الوصف: | Biomedical argument mining aims to automatically identify and extract the argumentative structure in biomedical text. It helps to determine not only what positions people adopt, but also why they hold such opinions, which provides valuable insights into medical decision making. Generally, biomedical argument mining consists of three subtasks: argument component identification, argument component classification and relation identification. Current approaches employ conventional multi-task learning framework for jointly addressing the latter two subtasks, and achieve some successes. However, explicit sequential dependency between these two subtasks is ignored, which is crucial for accurate biomedical argument mining. Moreover, relation identification is conducted solely based on the argument component pair without considering its potentially valuable context. Therefore, in this paper, a novel sequential multi-task learning approach is proposed for biomedical argument mining. Specifically, to model explicit sequential dependency between argument component classification and relation identification, an information transfer strategy is employed to capture the information of argument component types that is transferred to relation identification. Furthermore, graph convolutional network is employed to model dependency relation among the related argument component pairs. The proposed method has been evaluated on a benchmark dataset and the experimental results show that the proposed method outperforms the state-of-the-art methods. |
| تدمد: | 2374-0043 1545-5963 |
| URL الوصول: | https://explore.openaire.eu/search/publication?articleId=doi_dedup___::5f4eb441d48807cea2030c8ba81c4a42 https://doi.org/10.1109/tcbb.2022.3173447 |
| حقوق: | CLOSED |
| رقم الأكسشن: | edsair.doi.dedup.....5f4eb441d48807cea2030c8ba81c4a42 |
| قاعدة البيانات: | OpenAIRE |
| تدمد: | 23740043 15455963 |
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