An Improved Approach for Semantic Graph Composition with CCG
Austin Blodgett, Nathan Schneider · 2019
This paper builds on previous work using Combinatory Categorial Grammar (CCG) to derive a transparent syntax-semantics interface for Abstract Meaning Representation (AMR) parsing.We define new semantics for the CCG combinators that is better suited to deriving AMR graphs.In particular, we define relation-wise alternatives for the application and composition combinators: these require that the two constituents being combined overlap in one AMR relation.We also provide a new semantics for type raising, which is necessary for certain constructions.Using these mechanisms, we suggest an analysis of eventive nouns, which present a challenge for deriving AMR graphs.Our theoretical analysis will facilitate future work on robust and transparent AMR parsing using CCG.