English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Plus monthly

Global: Zendy Tools annual

Global: Zendy Tools monthly

Global: Zendy Free Plan

Clinical Named Entity Recognition (CNER) aims to identify and classifyclinical terms such as diseases, symptoms, treatments, exams, and body parts inelectronic health records, which is a fundamental and crucial task for clinicaland translational research. In recent years, deep neural networks have achievedsignificant success in named entity recognition and many other Natural LanguageProcessing (NLP) tasks. Most of these algorithms are trained end to end, andcan automatically learn features from large scale labeled datasets. However,these data-driven methods typically lack the capability of processing rare orunseen entities. Previous statistical methods and feature engineering practicehave demonstrated that human knowledge can provide valuable information forhandling rare and unseen cases. In this paper, we address the problem byincorporating dictionaries into deep neural networks for the Chinese CNER task.Two different architectures that extend the Bi-directional Long Short-TermMemory (Bi-LSTM) neural network and five different feature representationschemes are proposed to handle the task. Computational results on the CCKS-2017Task 2 benchmark dataset show that the proposed method achieves the highlycompetitive performance compared with the state-of-the-art deep learningmethods.

Incorporating Dictionaries into Deep Neural Networks for the Chinese Clinical Named Entity Recognition