用短语特征增强文本图神经网络，提升教育问答分类效果
PQ-GCN: Enhancing Text Graph Question Classification with Phrase Features
- 构建含短语特征的文本图，用GCN捕捉语义关系
- 在低资源场景下优于基线图模型，参数量仅为语言模型的几分之一
- 适合教育类智能系统，兼顾准确率与计算效率
有效的问题分类对AI驱动的教育工具至关重要，有助于自适应学习系统按技能领域、难度等级和能力水平对问题进行归类。这不仅支持教育诊断与分析，还通过关联问题与相关类别来增强信息检索和问答等下游任务。传统方法多基于词嵌入和常规分类器，难以捕捉问题陈述中的细微语义关系，导致性能不佳。本文提出一种新方法——短语问题图卷积网络（PQ-GCN），通过引入短语级特征，评估其在不同领域和特性的问答数据集上的分类表现。所提方法在低资源环境下优于基线图模型，在参数量仅为语言模型几分之一的情况下，仍能与主流语言模型方法竞争。研究结果为更上下文感知、参数高效的问答分类提供了可行方案，弥合了图神经网络研究与教育应用之间的差距。
原文摘要 · Abstract (English)
Effective question classification is crucial for AI-driven educational tools, enabling adaptive learning systems to categorize questions by skill area, difficulty level, and competence. It not only supports educational diagnostics and analytics but also enhances complex downstream tasks like information retrieval and question answering by associating questions with relevant categories. Traditional methods, often based on word embeddings and conventional classifiers, struggle to capture the nuanced relationships in question statements, leading to suboptimal performance. We propose a novel approach leveraging graph convolutional networks, named Phrase Question-Graph Convolutional Network (PQ-GCN). Through PQ-GCN, we evaluate the incorporation of phrase-based features to enhance classification performance on question datasets of various domains and characteristics. The proposed method, augmented with phrase-based features, outperform baseline graph-based methods in low-resource settings, and performs competitively against language model-based methods with a fraction of their parameter size. Our findings offer a possible solution for more context-aware, parameter-efficient question classification, bridging the gap between graph neural network research and its educational applications.
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