PQ-GCN：利用短语特征增强文本图问题分类
PQ-GCN: Enhancing Text Graph Question Classification with Phrase Features
AI总结：
提出PQ-GCN方法，通过融入短语特征增强图卷积网络，在低资源设置下优于基线图方法，并以极小参数规模与语言模型方法竞争，为教育场景提供了上下文感知且参数高效的问题分类方案。
摘要：
有效的问题分类对AI驱动的教育工具至关重要，使自适应学习系统能按技能领域、难度水平和能力对问题进行分类。它不仅支持教育诊断和分析，还通过将问题与相关类别关联，增强了信息检索和问答等复杂的下游任务。传统方法通常基于词嵌入和常规分类器，难以捕捉问题陈述中的细微关系，导致性能不佳。我们提出了一种利用图卷积网络的新方法，命名为短语问题-图卷积网络（PQ-GCN）。通过PQ-GCN，我们评估了融入基于短语的特征以增强在各种领域和特征的问题数据集上分类性能的效果。该方法在融入基于短语的特征后，在低资源设置下优于基线图方法，并且在与基于语言模型的方法竞争时表现优异，而其参数规模仅为后者的一小部分。我们的发现为更具上下文感知能力、参数高效的问题分类提供了可能的解决方案，弥合了图神经网络研究与其教育应用之间的差距。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
