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

Automatic annotation of short-text data to a large number of target labels,referred to as Short Text Extreme Classification, has found numerousapplications including prediction of related searches and productrecommendation tasks. In this paper, we propose a convolutional architectureInceptionXML which is light-weight, yet powerful, and robust to the inherentlack of word-order in short-text queries encountered in search andrecommendation tasks. We demonstrate the efficacy of applying convolutions byrecasting the operation along the embedding dimension instead of the worddimension as applied in conventional CNNs for text classification. Towardsscaling our model to datasets with millions of labels, we also proposeInceptionXML+ framework which improves upon the shortcomings of the recentlyproposed dynamic hard-negative mining technique for label shortlisting bysynchronizing the label-shortlister and extreme classifier. InceptionXML+ notonly reduces the inference time to half but is also an order of magnitudesmaller than previous state-of-the-art Astec in terms of model size. Throughour proposed models, we outperform all existing approaches on popular benchmarkdatasets.

InceptionXML: A Lightweight Framework with Synchronized Negative Sampling for Short Text Extreme Classification