# Graph convolution-based techniques for pragmatic Arabic figurative language classification

## TL;DR

This paper presents a graph-based method for classifying figurative language in Arabic, outperforming existing models by capturing linguistic structure and semantics.

## Contribution

A novel graph-based embedding framework for figurative language classification in Arabic using heterogeneous graphs and GNNs.

## Key findings

- Attention-based and multi-hop GNNs outperform transformer models in Arabic figurative language tasks.
- HANConv and GAT achieve the highest F1-scores for euphemism and metonymy detection.
- Graph-structured embeddings show potential for nuanced linguistic analysis in low-resource languages.

## Abstract

Figurative language, including euphemism and metonymy, presents significant challenges in natural language processing (NLP) due to its abstract and context-dependent nature, particularly in morphologically rich and low-resource languages like Arabic. This paper introduces a graph-based embedding framework for figurative language classification that captures both syntactic dependencies and semantic relationships using heterogeneous graphs. We propose a configurable pipeline that converts text into structured graphs incorporating lexical, morphological, and syntactic cues, enabling deeper semantic reasoning. These graphs are processed using various graph neural network (GNN) architectures—such as GAT, HANConv, and MixHopConv—designed to model complex linguistic interactions. The approach is evaluated on two Arabic-language tasks: euphemism and metonymy detection. Our results demonstrate that attention-based and multi-hop GNNs outperform both traditional baselines and state-of-the-art transformer models (e.g., AraBERT, XLM-RoBERTa), particularly in metonymy detection where topological cues are more pronounced. HANConv and GAT achieve the highest F1-scores across tasks, while models like GraphConv and SAGEConv offer stability across configurations. We also introduce a validated Arabic lexical ontology for enriching semantic graphs. Our findings highlight the potential of graph-structured embeddings for nuanced linguistic tasks and suggest future directions including cross-lingual transfer, ontology expansion, and application to additional figurative categories.