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

In the new era of internet systems and applications, a concept of detectingdistinguished topics from huge amounts of text has gained a lot of attention.These methods use representation of text in a numerical format -- calledembeddings -- to imitate human-based semantic similarity between words. In thisstudy, we perform a fuzzy-based analysis of various vector representations ofwords, i.e., word embeddings. Also we introduce new methods of fuzzy clusteringbased on hybrid implementation of fuzzy clustering methods with an evolutionaryalgorithm named Forest Optimization. We use two popular fuzzy clusteringalgorithms on count-based word embeddings, with different methods anddimensionality. Words about covid from Kaggle dataset gathered and calculatedinto vectors and clustered. The results indicate that fuzzy clusteringalgorithms are very sensitive to high-dimensional data, and parameter tuningcan dramatically change their performance. We evaluate results of experimentswith various clustering validity indexes to compare different algorithmvariation with different embeddings accuracy.

Word Embeddings and Validity Indexes in Fuzzy Clustering