A Neural Multi-Task Learning Framework to Jointly Model Medical Named
Entity Recognition and Normalization
release_kbgrukywhfh67npm3gzx6z23zu
by
Sendong Zhao, Ting Liu, Sicheng Zhao, Fei Wang
2018
Abstract
State-of-the-art studies have demonstrated the superiority of joint modelling
over pipeline implementation for medical named entity recognition and
normalization due to the mutual benefits between the two processes. To exploit
these benefits in a more sophisticated way, we propose a novel deep neural
multi-task learning framework with explicit feedback strategies to jointly
model recognition and normalization. On one hand, our method benefits from the
general representations of both tasks provided by multi-task learning. On the
other hand, our method successfully converts hierarchical tasks into a parallel
multi-task setting while maintaining the mutual supports between tasks. Both of
these aspects improve the model performance. Experimental results demonstrate
that our method performs significantly better than state-of-the-art approaches
on two publicly available medical literature datasets.
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