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

Rapidly developed neural models have achieved competitive performance inChinese word segmentation (CWS) as their traditional counterparts. However,most of methods encounter the computational inefficiency especially for longsentences because of the increasing model complexity and slower decoders. Thispaper presents a simple neural segmenter which directly labels the gapexistence between adjacent characters to alleviate the existing drawback. Oursegmenter is fully end-to-end and capable of performing segmentation very fast.We also show a performance difference with different tag sets. The experimentsshow that our segmenter can provide comparable performance withstate-of-the-art.

Fast Neural Chinese Word Segmentation for Long Sentences