A Neural Multi-Task Learning Framework to Jointly Model Medical Named Entity Recognition and Normalization

Understand

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.