Connecting the Dots: What Graph-Based Text Representations Work Best for Text Classification using Graph Neural Networks?

Margarita Bugueño, Gerard de Melo

Submission Type: Regular Long Paper

Submission Track: Information Retrieval and Text Mining

Keywords: Graph-Based Text Representation, Graph Neural Networks, Text Classification

TL;DR: An empirical investigation of graph representations for text classification using GNNs.

Abstract:

Given the success of Graph Neural Networks (GNNs) for structure-aware machine learning, many studies have explored their use for text classification, but mostly in specific domains with limited data characteristics. Moreover, some strategies prior to GNNs relied on graph mining and classical machine learning, making it difficult to assess their effectiveness in modern settings.
This work extensively investigates graph representation methods for text classification, identifying practical implications and open challenges.
We compare different graph construction schemes using a variety of GNN architectures and setups across five datasets, encompassing short and long documents as well as unbalanced scenarios in diverse domains.
Two Transformer-based large language models are also included to complement the study.
The results show that i) although the effectiveness of graphs depends on the textual input features and domain, simple graph constructions perform better the longer the documents are, ii) graph representations are especially beneficial for longer documents, outperforming Transformer-based models, iii) graph methods are particularly efficient for solving the task.

Submission Number: 4298