用知识图谱增强文本编码，提升假信息检测准确率
TEGRA: Text Encoding With Graph and Retrieval Augmentation for Misinformation Detection
- 将文本转为知识图谱，融合结构化信息与语义表示
- 相比纯语言模型，假信息检测准确率显著提升
- 适合需要事实验证的新闻、社交平台内容审核场景
假信息检测是一项关键任务，可从外部知识中获益，类似人工核查。本文提出一种新型文本表示方法——文本编码图（TEG），通过提取文档中的结构化信息构建知识图谱，并联合编码文本与图谱进行分类。大量实验表明，这种混合表示相比仅使用语言模型显著提升了假信息检测性能。进一步提出TEGRA框架，引入领域特定知识，在多数情况下进一步提高了分类准确率。
原文摘要 · Abstract (English)
Misinformation detection is a critical task that can benefit significantly from the integration of external knowledge, much like manual fact-checking. In this work, we propose a novel method for representing textual documents that facilitates the incorporation of information from a knowledge base. Our approach, Text Encoding with Graph (TEG), processes documents by extracting structured information in the form of a graph and encoding both the text and the graph for classification purposes. Through extensive experiments, we demonstrate that this hybrid representation enhances misinformation detection performance compared to using language models alone. Furthermore, we introduce TEGRA, an extension of our framework that integrates domain-specific knowledge, further enhancing classification accuracy in most cases.
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