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

Most NLP applications work under the assumption that a user input is error-free; thus, word segmentation (WS) for written languages that use word boundary markers (WBMs), such as spaces, has been regarded as a trivial issue. However, noisy real-world texts, such as blogs, e-mails, and SMS, may contain spacing errors that require correction before further processing may take place. For the Korean language, many researchers have adopted a traditional WS approach, which eliminates all spaces in the user input and re-inserts proper word boundaries. Unfortunately, such an approach often exacerbates the word spacing quality for user input, which has few or no spacing errors; such is the case, because a perfect WS model does not exist. In this paper, we propose a novel WS method that takes into consideration the initial word spacing information of the user input. Our method generates a better output than the original user input, even if the user input has few spacing errors. Moreover, the proposed method significantly outperforms a state-of-the-art Korean WS model when the user input initially contains less than 10% spacing errors, and performs comparably for cases containing more spacing errors. We believe that the proposed method will be a very practical pre-processing module.

A novel word segmentation approach for written languages with word boundary markers