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

The importance of an efficient and scalable document similarity detectionsystem is undeniable nowadays. Search engines need batch text similaritymeasures to detect duplicated and near-duplicated web pages in their indexes inorder to prevent indexing a web page multiple times. Furthermore, in thescoring phase, search engines need similarity measures to detect duplicatedcontents on web pages so as to increase the quality of their results. In thispaper, a new approach to batch text similarity detection is proposed bycombining some ideas from dimensionality reduction techniques and informationgain theory. The new approach is focused on search engines need to detectduplicated and near-duplicated web pages. The new approach is evaluated on theNEWS20 dataset and the results show that the new approach is faster than thecosine text similarity algorithm in terms of speed and performance. On top ofthat, It is faster and more accurate than the other rival method, Simhashsimilarity algorithm.

Multi-reference Cosine: A New Approach to Text Similarity Measurement in Large Collections