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

In the Text Classification areas of Sentiment Analysis,Subjectivity/Objectivity Analysis, and Opinion Polarity, Convolutional NeuralNetworks have gained special attention because of their performance andaccuracy. In this work, we applied recent advances in CNNs and propose a novelarchitecture, Multiple Block Convolutional Highways (MBCH), which achievesimproved accuracy on multiple popular benchmark datasets, compared to previousarchitectures. The MBCH is based on new techniques and architectures includinghighway networks, DenseNet, batch normalization and bottleneck layers. Inaddition, to cope with the limitations of existing pre-trained word vectorswhich are used as inputs for the CNN, we propose a novel method, Improved WordVectors (IWV). The IWV improves the accuracy of CNNs which are used for textclassification tasks.

Text Classification based on Multiple Block Convolutional Highways