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

The classification of news headlines is an important direction in the fieldof NLP, and its data has the characteristics of compactness, uniqueness andvarious forms. Aiming at the problem that the traditional neural network modelcannot adequately capture the underlying feature information of the data andcannot jointly extract key global features and deep local features, adual-channel network model DC-EBAD based on the ERNIE pre-training model isproposed. Use ERNIE to extract the lexical, semantic and contextual featureinformation at the bottom of the text, generate dynamic word vectorrepresentations fused with context, and then use the BiLSTM-AT network channelto secondary extract the global features of the data and use the attentionmechanism to give key parts higher The weight of the DPCNN channel is used toovercome the long-distance text dependence problem and obtain deep localfeatures. The local and global feature vectors are spliced, and finally passedto the fully connected layer, and the final classification result is outputthrough Softmax. The experimental results show that the proposed model improvesthe accuracy, precision and F1-score of news headline classification comparedwith the traditional neural network model and the single-channel model underthe same conditions. It can be seen that it can perform well in themulti-classification application of news headline text under large data volume.

Research on Dual Channel News Headline Classification Based on ERNIE Pre-training Model