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 Tools annual

Global: Zendy Free Plan

Global: Zendy Tools monthly

Global: Zendy Plus monthly

This paper proposes a chatbot framework that adopts a hybrid model whichconsists of a knowledge graph and a text similarity model. Based on thischatbot framework, we build HHH, an online question-and-answer (QA) HealthcareHelper system for answering complex medical questions. HHH maintains aknowledge graph constructed from medical data collected from the Internet. HHHalso implements a novel text representation and similarity deep learning model,Hierarchical BiLSTM Attention Model (HBAM), to find the most similar questionfrom a large QA dataset. We compare HBAM with other state-of-the-art languagemodels such as bidirectional encoder representation from transformers (BERT)and Manhattan LSTM Model (MaLSTM). We train and test the models with a subsetof the Quora duplicate questions dataset in the medical area. The experimentalresults show that our model is able to achieve a superior performance thanthese existing methods.

HHH: An Online Medical Chatbot System based on Knowledge Graph and Hierarchical Bi-Directional Attention