# An Improved Approach for Semantic Graph Composition with CCG

## TL;DR

This paper enhances CCG-based semantic parsing for AMR by introducing new combinator semantics, improving the derivation of AMR graphs, especially for complex constructions like eventive nouns.

## Contribution

It proposes novel relation-wise combinator semantics and a new type raising semantics to improve AMR parsing with CCG.

## Key findings

- Defines relation-wise combinators for better AMR graph derivation
- Introduces new semantics for type raising in CCG
- Provides analysis of eventive nouns in AMR parsing

## Abstract

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.

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Source: https://tomesphere.com/paper/1903.11770