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

Word segmentation is a fundamental pre-processing step for Thai NaturalLanguage Processing. The current off-the-shelf solutions are not benchmarkedconsistently, so it is difficult to compare their trade-offs. We conducted aspeed and accuracy comparison of the popular systems on three different domainsand found that the state-of-the-art deep learning system is slow and moreoverdoes not use sub-word structures to guide the model. Here, we propose a fastand accurate neural Thai Word Segmenter that uses dilated CNN filters tocapture the environment of each character and uses syllable embeddings asfeatures. Our system runs at least 5.6x faster and outperforms the previousstate-of-the-art system on some domains. In addition, we develop the firstML-based Thai orthographical syllable segmenter, which yields syllableembeddings to be used as features by the word segmenter.

AttaCut: A Fast and Accurate Neural Thai Word Segmenter