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

This paper presents an approach to FAQ mining via a list detection algorithm. List detection is very important for data collection since list has been widely used for representing data and information on the Web. By analyzing the rendering of FAQs on the Web, we found a fact that all FAQs are always fully/partially represented in a list-like form. There are two ways to author a list on the Web. One is to use some specific tags, e.g. tag for HTML. The lists authored in this way can be easily detected by parsing those special tags. Another way uses other tags instead of the special tags. Unfortunately, many lists are authored in the second way. To detect lists, therefore, we present an algorithm, which is independent of Web languages. By combining the algorithm with some domain knowledge, we detect and collect FAQs from the Web. The mining task achieved a performance of 72.54% recall and 80.16% precision rates.

FAQ mining via list detection