  Clinical Named Entity Recognition (CNER) aims to identify and classify
clinical terms such as diseases, symptoms, treatments, exams, and body parts in
electronic health records, which is a fundamental and crucial task for clinical
and translational research. In recent years, deep neural networks have achieved
significant success in named entity recognition and many other Natural Language
Processing (NLP) tasks. Most of these algorithms are trained end to end, and
can automatically learn features from large scale labeled datasets. However,
these data-driven methods typically lack the capability of processing rare or
unseen entities. Previous statistical methods and feature engineering practice
have demonstrated that human knowledge can provide valuable information for
handling rare and unseen cases. In this paper, we address the problem by
incorporating dictionaries into deep neural networks for the Chinese CNER task.
Two different architectures that extend the Bi-directional Long Short-Term
Memory (Bi-LSTM) neural network and five different feature representation
schemes are proposed to handle the task. Computational results on the CCKS-2017
Task 2 benchmark dataset show that the proposed method achieves the highly
competitive performance compared with the state-of-the-art deep learning
methods.
