#6160. Syntax-based transfer learning for the task of biomedical relation extraction

September 2026publication date
Proposal available till 27-05-2025
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Journal’s subject area:
Computer Networks and Communications;
Computer Science Applications;
Information Systems;
Health Informatics;
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Abstract:
Transfer learning aims at enhancing machine learning performance on a problem by reusing labeled data originally designed for a related, but distinct problem. In particular, domain adaptation consists for a specific task, in reusing training data developedfor the same task but a distinct domain. This is particularly relevant to the applications of deep learning in Natural Language Processing, because they usually require large annotated corpora that may not exist for the targeted domain, but exist for side domains. In this paper, we experiment with transfer learning for the task of relation extraction from biomedical texts, using the TreeLSTM model. We empirically show the impact of TreeLSTM alone and with domain adaptation by obtaining better performances than the state of the art on two biomedical relation extraction tasks and equal performances for two others, for which little annotated data are available. Furthermore, we propose an analysis of the role that syntactic features may play in transfer learning for relation extraction.
Keywords:
Biomedical relation extraction; Deep learning; Transfer learning

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