用可解释AI提取数学题中的实体关系，准确率达99.39%。
Transparent AI for Mathematics: Transformer-Based Large Language Models for Mathematical Entity Relationship Extraction with XAI
- 将数学运算符视为关系，实体为操作数，用BERT模型自动抽取
- 在特定数据集上达到99.39%的准确率，显著优于传统方法
- 结合SHAP分析特征重要性，适合教育系统与智能辅导场景
由于数学文本中存在专业实体及复杂关系，理解难度较高。本文将数学问题解析定义为数学实体关系抽取（MERE）任务，将操作数视为实体，运算符视为其关系。采用基于Transformer的模型从数学文本中自动提取这些关系，其中双向编码器表示模型（BERT）表现最佳，准确率达到99.39%。为提升模型预测的透明度与可信度，引入可解释人工智能（XAI）技术，使用加性解释（SHAP）进行分析，揭示特定文本与数学特征对关系预测的影响，提供特征重要性与模型行为的洞察。通过融合Transformer学习、专用数据集与可解释建模，本研究构建了一个高效且可解释的MERE框架，可支持自动化解题、知识图谱构建与智能教育系统等应用。
原文摘要 · Abstract (English)
Mathematical text understanding is a challenging task due to the presence of specialized entities and complex relationships between them. This study formulates mathematical problem interpretation as a Mathematical Entity Relation Extraction (MERE) task, where operands are treated as entities and operators as their relationships. Transformer-based models are applied to automatically extract these relations from mathematical text, with Bidirectional Encoder Representations from Transformers (BERT) achieving the best performance, reaching an accuracy of 99.39%. To enhance transparency and trust in the model's predictions, Explainable Artificial Intelligence (XAI) is incorporated using Shapley Additive Explanations (SHAP). The explainability analysis reveals how specific textual and mathematical features influence relation prediction, providing insights into feature importance and model behavior. By combining transformer-based learning, a task-specific dataset, and explainable modeling, this work offers an effective and interpretable framework for MERE, supporting future applications in automated problem solving, knowledge graph construction, and intelligent educational systems.
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