Abstract
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.
Abstract (translated)
数学文本理解是一个具有挑战性的任务，因为其中包含专业的实体以及这些实体之间复杂的关系。这项研究将数学问题解释定义为一个数学实体关系抽取（MERE）的任务，在这个过程中，操作数被视为实体，而运算符则作为它们之间的关系。基于Transformer的模型被应用于自动从数学文本中提取这些关系，并且双向编码器表示法（BERT）表现最佳，达到了99.39%的准确率。 为了提高模型预测的透明度和可信度，研究采用了可解释的人工智能（XAI），具体使用了Shapley Additive Explanations（SHAP）。通过这种可解释性分析，可以揭示特定文本特征和数学特性如何影响关系预测，并提供有关特征重要性和模型行为的见解。 这项工作结合了基于Transformer的学习、任务特异性数据集以及可解释建模方法，为MERE提供了有效且可解释的框架。这将支持未来在自动化问题解决、知识图谱构建以及智能教育系统中的应用。
URL
https://arxiv.org/abs/2603.06348
