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

The present article reports on efforts to improve the translation accuracy of a corpus– based hybrid MT system developed using the PRESEMT methodology. This methodology operates on a phrasal basis, where phrases are linguistically-motivated but are automatically determined via a dedicated module. Here, emphasis is placed on improving the structure of each translated sentence, by replacing the Example-Based MT approach originally used in PRESEMT with a sub-sentential approach. Results indicate that an improved accuracy can be achieved, as measured by objective metrics.

Establishing sentential structure via realignments from small parallel corpora