  Recognizing useful named entities plays a vital role in medical information
processing, which helps drive the development of medical area research. Deep
learning methods have achieved good results in medical named entity recognition
(NER). However, we find that existing methods face great challenges when
dealing with the nested named entities. In this work, we propose a novel
method, referred to as ASAC, to solve the dilemma caused by the nested
phenomenon, in which the core idea is to model the dependency between different
categories of entity recognition. The proposed method contains two key modules:
the adaptive shared (AS) part and the attentive conditional random field (ACRF)
module. The former part automatically assigns adaptive weights across each task
to achieve optimal recognition accuracy in the multi-layer network. The latter
module employs the attention operation to model the dependency between
different entities. In this way, our model could learn better entity
representations by capturing the implicit distinctions and relationships
between different categories of entities. Extensive experiments on public
datasets verify the effectiveness of our method. Besides, we also perform
ablation analyses to deeply understand our methods.
