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 eXtreme Multi-label Classification~(XMC) problem seeks to find relevantlabels from an exceptionally large label space. Most of the existing XMClearners focus on the extraction of semantic features from input query text.However, conventional XMC studies usually neglect the side information ofinstances and labels, which can be of use in many real-world applications suchas recommendation systems and e-commerce product search. We propose PredictedInstance Neighborhood Aggregation (PINA), a data enhancement method for thegeneral XMC problem that leverages beneficial side information. Unlike mostexisting XMC frameworks that treat labels and input instances as featurelessindicators and independent entries, PINA extracts information from the labelmetadata and the correlations among training instances. Extensive experimentalresults demonstrate the consistent gain of PINA on various XMC tasks comparedto the state-of-the-art methods: PINA offers a gain in accuracy compared tostandard XR-Transformers on five public benchmark datasets. Moreover, PINAachieves a $\sim 5\%$ gain in accuracy on the largest datasetLF-AmazonTitles-1.3M. Our implementation is publicly available.

PINA: Leveraging Side Information in eXtreme Multi-label Classification via Predicted Instance Neighborhood Aggregation