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/

Zendy Plus

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

Zendy Tools

Zendy Open

Consumer Event-Cause Extraction, the task aimed at extracting the potentialcauses behind certain events in the text, has gained much attention in recentyears due to its wide applications. The ICDM 2020 conference sets up anevaluation competition that aims to extract events and the causes of theextracted events with a specified subject (a brand or product). In this task,we mainly focus on how to construct an end-to-end model, and extract multipleevent types and event-causes simultaneously. To this end, we introduce a freshperspective to revisit the relational event-cause extraction task and propose anovel sequence tagging framework, instead of extracting event types andevents-causes separately. Experiments show our framework outperforms baselinemethods even when its encoder module uses an initialized pre-trained BERTencoder, showing the power of the new tagging framework. In this competition,our team achieved 1st place in the first stage leaderboard, and 3rd place inthe final stage leaderboard.

ICDM 2020 Knowledge Graph Contest: Consumer Event-Cause Extraction