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

During crisis events, people often use social media platforms such as Twitterto disseminate information about the situation, warnings, advice, and support.Emergency relief organizations leverage such information to acquire timelycrisis circumstances and expedite rescue operations. While existing worksutilize such information to build models for crisis event analysis,fully-supervised approaches require annotating vast amounts of data and areimpractical due to limited response time. On the other hand, semi-supervisedmodels can be biased, performing moderately well for certain classes whileperforming extremely poorly for others, resulting in substantially negativeeffects on disaster monitoring and rescue. In this paper, we first study tworecent debiasing methods on semi-supervised crisis tweet classification. Thenwe propose a simple but effective debiasing method, DeCrisisMB, that utilizes aMemory Bank to store and perform equal sampling for generated pseudo-labelsfrom each class at each training iteration. Extensive experiments are conductedto compare different debiasing methods' performance and generalization abilityin both in-distribution and out-of-distribution settings. The resultsdemonstrate the superior performance of our proposed method. Our code isavailable at https://github.com/HenryPengZou/DeCrisisMB.

DeCrisisMB: Debiased Semi-Supervised Learning for Crisis Tweet Classification via Memory Bank