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

Event schemas are a form of world knowledge about the typical progression ofevents. Recent methods for event schema induction use information extractionsystems to construct a large number of event graph instances from documents,and then learn to generalize the schema from such instances. In contrast, wepropose to treat event schemas as a form of commonsense knowledge that can bederived from large language models (LLMs). This new paradigm greatly simplifiesthe schema induction process and allows us to handle both hierarchicalrelations and temporal relations between events in a straightforward way. Sinceevent schemas have complex graph structures, we design an incremental promptingand verification method to break down the construction of a complex event graphinto three stages: event skeleton construction, event expansion, andevent-event relation verification. Compared to directly using LLMs to generatea linearized graph, our method can generate large and complex schemas with 7.2%F1 improvement in temporal relations and 31.0% F1 improvement in hierarchicalrelations. In addition, compared to the previous state-of-the-art closed-domainschema induction model, human assessors were able to cover $\sim$10% moreevents when translating the schemas into coherent stories and rated our schemas1.3 points higher (on a 5-point scale) in terms of readability.

Open-Domain Hierarchical Event Schema Induction by Incremental Prompting and Verification