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 consider a large number of text data sets. These are cooking recipes. Termdistribution and other distributional properties of the data are investigated.Our aim is to look at various analytical approaches which allow for mining ofinformation on both high and low detail scales. Metric space embedding isfundamental to our interest in the semantic properties of this data. Weconsider the projection of all data into analyses of aggregated versions of thedata. We contrast that with projection of aggregated versions of the data intoanalyses of all the data. Analogously for the term set, we look at analysis ofselected terms. We also look at inherent term associations such as betweensingular and plural. In addition to our use of Correspondence Analysis in R,for latent semantic space mapping, we also use Apache Solr. Setting up the Solrserver and carrying out querying is described. A further novelty is thatquerying is supported in Solr based on the principal factor plane mapping ofall the data. This uses a bounding box query, based on factor projections.

Correspondence Factor Analysis of Big Data Sets: A Case Study of 30  Million Words; and Contrasting Analytics using Apache Solr and Correspondence  Analysis in R