Joint Extraction of Uyghur Medicine Knowledge with Edge Computing
Fan Lu Quan Qi Huaibin Qin
Medical knowledge extraction methods based on edge computing deploy deep
learning models on edge devices to achieve localized entity and relation
extraction. This approach avoids transferring substantial sensitive data to
cloud data centers, effectively safeguarding the privacy of healthcare
services. However, existing relation extraction methods mainly employ a
sequential pipeline approach, which classifies relations between determined
entities after entity recognition. This mode faces challenges such as error
propagation between tasks, insufficient consideration of dependencies between
the two subtasks, and the neglect of interrelations between different relations
within a sentence. To address these challenges, a joint extraction model with
parameter sharing in edge computing is proposed, named CoEx-Bert. This model
leverages shared parameterization between two models to jointly extract
entities and relations. Specifically, CoEx-Bert employs two models, each
separately sharing hidden layer parameters, and combines these two loss
functions for joint backpropagation to optimize the model parameters.
Additionally, it effectively resolves the issue of entity overlapping when
extracting knowledge from unstructured Uyghur medical texts by considering
contextual relations. Finally, this model is deployed on edge devices for
real-time extraction and inference of Uyghur medical knowledge. Experimental
results demonstrate that CoEx-Bert outperforms existing state-of-the-art
methods, achieving accuracy, recall, and F1 scores of 90.65\%, 92.45\%, and
91.54\%, respectively, in the Uyghur traditional medical literature dataset.
These improvements represent a 6.45\% increase in accuracy, a 9.45\% increase
in recall, and a 7.95\% increase in F1 score compared to the baseline.