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

We propose Quootstrap, a method for extracting quotations, as well as thenames of the speakers who uttered them, from large news corpora. Whereas priorwork has addressed this problem primarily with supervised machine learning, ourapproach follows a fully unsupervised bootstrapping paradigm. It leverages theredundancy present in large news corpora, more precisely, the fact that thesame quotation often appears across multiple news articles in slightlydifferent contexts. Starting from a few seed patterns, such as ["Q", said S.],our method extracts a set of quotation-speaker pairs (Q, S), which are in turnused for discovering new patterns expressing the same quotations; the processis then repeated with the larger pattern set. Our algorithm is highly scalable,which we demonstrate by running it on the large ICWSM 2011 Spinn3r corpus.Validating our results against a crowdsourced ground truth, we obtain 90%precision at 40% recall using a single seed pattern, with significantly higherrecall values for more frequently reported (and thus likely more interesting)quotations. Finally, we showcase the usefulness of our algorithm's output forcomputational social science by analyzing the sentiment expressed in ourextracted quotations.

Quootstrap: Scalable Unsupervised Extraction of Quotation-Speaker Pairs from Large News Corpora via Bootstrapping