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

Recent advances in weakly supervised text classification mostly focus ondesigning sophisticated methods to turn high-level human heuristics intoquality pseudo-labels. In this paper, we revisit the seed matching-basedmethod, which is arguably the simplest way to generate pseudo-labels, and showthat its power was greatly underestimated. We show that the limited performanceof seed matching is largely due to the label bias injected by the simpleseed-match rule, which prevents the classifier from learning reliableconfidence for selecting high-quality pseudo-labels. Interestingly, simplydeleting the seed words present in the matched input texts can mitigate thelabel bias and help learn better confidence. Subsequently, the performanceachieved by seed matching can be improved significantly, making it on par withor even better than the state-of-the-art. Furthermore, to handle the case whenthe seed words are not made known, we propose to simply delete the word tokensin the input text randomly with a high deletion ratio. Remarkably, seedmatching equipped with this random deletion method can often achieve evenbetter performance than that with seed deletion.

Debiasing Made State-of-the-art: Revisiting the Simple Seed-based Weak Supervision for Text Classification