UCCA Parsing
4 papers with code • 2 benchmarks • 1 datasets
UCCA (Abend and Rappoport, 2013) is a semantic representation whose main design principles are ease of annotation, cross-linguistic applicability, and a modular architecture. UCCA represents the semantics of linguistic utterances as directed acyclic graphs (DAGs), where terminal (childless) nodes correspond to the text tokens, and non-terminal nodes to semantic units that participate in some super-ordinate relation. Edges are labeled, indicating the role of a child in the relation the parent represents. UCCA’s foundational layer mostly covers predicate-argument structure, semantic heads and inter-Scene relations. UCCA distinguishes primary edges, corresponding to explicit relations, from remote edges that allow for a unit to participate in several super-ordinate relations. Primary edges form a tree in each layer, whereas remote edges enable reentrancy, forming a DAG.
Description from NLP Progress
Benchmarks
These leaderboards are used to track progress in UCCA Parsing
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CoNLL
Most implemented papers
Content Differences in Syntactic and Semantic Representation
Syntactic analysis plays an important role in semantic parsing, but the nature of this role remains a topic of ongoing debate.
A Transition-Based Directed Acyclic Graph Parser for UCCA
We present the first parser for UCCA, a cross-linguistically applicable framework for semantic representation, which builds on extensive typological work and supports rapid annotation.
Multitask Parsing Across Semantic Representations
The ability to consolidate information of different types is at the core of intelligence, and has tremendous practical value in allowing learning for one task to benefit from generalizations learned for others.
Content Differences in Syntactic and Semantic Representations
Syntactic analysis plays an important role in semantic parsing, but the nature of this role remains a topic of ongoing debate.
