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

Inspired by early research on exploring naturally annotated data for Chineseword segmentation (CWS), and also by recent research on integration of speechand text processing, this work for the first time proposes to mine wordboundaries from parallel speech/text data. First we collect parallelspeech/text data from two Internet sources that are related with CWS data usedin our experiments. Then, we obtain character-level alignments and designsimple heuristic rules for determining word boundaries according to pauseduration between adjacent characters. Finally, we present an effectivecomplete-then-train strategy that can better utilize extra naturally annotateddata for model training. Experiments demonstrate our approach can significantlyboost CWS performance in both cross-domain and low-resource scenarios.

Mining Word Boundaries in Speech as Naturally Annotated Word Segmentation Data