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

Information content (IC) based measures for finding semantic similarity isgaining preferences day by day. Semantics of concepts can be highlycharacterized by information theory. The conventional way for calculating IC isbased on the probability of appearance of concepts in corpora. Due to datasparseness and corpora dependency issues of those conventional approaches, anew corpora independent intrinsic IC calculation measure has evolved. In thispaper, we mainly focus on such intrinsic IC model and several topologicalaspects of the underlying ontology. Accuracy of intrinsic IC calculation andsemantic similarity measure rely on these aspects deeply. Based on theseanalysis we propose an information theoretic framework which comprises anintrinsic IC calculator and a semantic similarity model. Our approach iscompared with state of the art semantic similarity measures based on corporadependent IC calculation as well as intrinsic IC based methods using severalbenchmark data set. We also compare our model with the related Edge based,Feature based and Distributional approaches. Experimental results show that ourintrinsic IC model gives high correlation value when applied to differentsemantic similarity models. Our proposed semantic similarity model alsoachieves significant results when embedded with some state of the art IC modelsincluding ours.

A Novel Information Theoretic Framework for Finding Semantic Similarity in WordNet