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

Frame semantic parsing is a semantic analysis task based on FrameNet whichhas received great attention recently. The task usually involves three subtaskssequentially: (1) target identification, (2) frame classification and (3)semantic role labeling. The three subtasks are closely related while previousstudies model them individually, which ignores their intern connections andmeanwhile induces error propagation problem. In this work, we propose anend-to-end neural model to tackle the task jointly. Concretely, we exploit agraph-based method, regarding frame semantic parsing as a graph constructionproblem. All predicates and roles are treated as graph nodes, and theirrelations are taken as graph edges. Experiment results on two benchmarkdatasets of frame semantic parsing show that our method is highly competitive,resulting in better performance than pipeline models.

A Graph-Based Neural Model for End-to-End Frame Semantic Parsing