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A deep learning model for biomedical semantic role labeling was build. Semantic role labeling is a useful task that enables the computer to comprehend the key facts expressed in each sentence, and is a necessary first step in the resolution of several other semantic-related tasks, such as event extraction, entity extraction, and Q-A systems... Semantic role labeling is a domain-dependent task. In the biomedical field, semantics are transmitted via more intricate grammatical structures and dependencies in addition to being built on a predicate argument frameset that differs greatly from that of the general domain. To effectively account for these unique characteristics, three types of information were integrated into this deep learning model: Context knowledge obtained from a pre-trained language model trained on a substantial corpus of biomedical texts, dependencies derived from the dependency parse trees and sentence structure obtained from constituency parse trees. To handle grammatical information that is naturally represented as graphs, the Graph Attention Network which is well-known for its remarkable graph learning capabilities, was used. To further boost the model effectiveness, predicate indicator embedding was additionally included in the proposed model. According to experimental findings, the two above-indicated forms of syntactic information along with the predicate indicator embedding, could boost F1 by up to 20%.

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Issue: Vol 7 No 4 (2023): Vol 7(4): Under publishing
Page No.: In press
Published: Nov 19, 2023
Section: Original Research

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Copyright: The Authors. This is an open access article distributed under the terms of the Creative Commons Attribution License CC-BY 4.0., which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

 How to Cite
Tuan Nguyen, H. D., Luu, T. D., & Huynh, Q. D. (2023). A syntax‒aware deep‒learning model for biomedical semantic role labelling. VNUHCM Journal of Natural Sciences, 7(4), In press.

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