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

Contextual embeddings represent a new generation of semantic representationslearned from Neural Language Modelling (NLM) that addresses the issue ofmeaning conflation hampering traditional word embeddings. In this work, we showthat contextual embeddings can be used to achieve unprecedented gains in WordSense Disambiguation (WSD) tasks. Our approach focuses on creating sense-levelembeddings with full-coverage of WordNet, and without recourse to explicitknowledge of sense distributions or task-specific modelling. As a result, asimple Nearest Neighbors (k-NN) method using our representations is able toconsistently surpass the performance of previous systems using powerful neuralsequencing models. We also analyse the robustness of our approach when ignoringpart-of-speech and lemma features, requiring disambiguation against the fullsense inventory, and revealing shortcomings to be improved. Finally, we exploreapplications of our sense embeddings for concept-level analyses of contextualembeddings and their respective NLMs.

Language Modelling Makes Sense: Propagating Representations through WordNet for Full-Coverage Word Sense Disambiguation