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 Tools annual

Global: Zendy Free Plan

Global: Zendy Tools monthly

Global: Zendy Plus monthly

To emphasize the fuzzy relation among words, latent concepts, text and topics, an information theory based approach to latent concept extraction and text clustering is proposed. Latent concept variable and topic variable are introduced to reveal such relation, and a global objective function is defined in the theme of rate-distortion theory. An anneal-like algorithm is designed to extract the hierarchical tree of latent concept, and to group the texts under corresponding concept hierarchy at the same time. Furthermore, it determines the number of concept and text clustering result with a concept selection method based on minimal description length criteria. It is a soft co-clustering method and outperforms the ones based on the word space, and current text hard co-clustering method based on latent concept by experiments.

Latent Concept Extraction and Text Clustering Based on Information Theory*