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

We describe our language-independent unsupervised word sense inductionsystem. This system only uses topic features to cluster different word sensesin their global context topic space. Using unlabeled data, this system trains alatent Dirichlet allocation (LDA) topic model then uses it to infer the topicsdistribution of the test instances. By clustering these topics distributions intheir topic space we cluster them into different senses. Our hypothesis is thatcloseness in topic space reflects similarity between different word senses.This system participated in SemEval-2 word sense induction and disambiguationtask and achieved the second highest V-measure score among all other systems.

KSU KDD: Word Sense Induction by Clustering in Topic Space