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Qualia Role-Based Quantity Relation Extraction for Solving Algebra Story Problems

Bin He, Hao Meng, Zhejin Zhang, Rui Liu, Ting Zhang*

Faculty of Artificial Intelligence in Education, Central China Normal University, Wuhan, 430079, China

* Corresponding Author: Ting Zhang. Email: email

(This article belongs to this Special Issue: Humanized Computing and Reasoning in Teaching and Learning)

Computer Modeling in Engineering & Sciences 2023, 136(1), 403-419.


A qualia role-based entity-dependency graph (EDG) is proposed to represent and extract quantity relations for solving algebra story problems stated in Chinese. Traditional neural solvers use end-to-end models to translate problem texts into math expressions, which lack quantity relation acquisition in sophisticated scenarios. To address the problem, the proposed method leverages EDG to represent quantity relations hidden in qualia roles of math objects. Algorithms were designed for EDG generation and quantity relation extraction for solving algebra story problems. Experimental result shows that the proposed method achieved an average accuracy of 82.2% on quantity relation extraction compared to 74.5% of baseline method. Another prompt learning result shows a 5% increase obtained in problem solving by injecting the extracted quantity relations into the baseline neural solvers.


Cite This Article

He, B., Meng, H., Zhang, Z., Liu, R., Zhang, T. (2023). Qualia Role-Based Quantity Relation Extraction for Solving Algebra Story Problems. CMES-Computer Modeling in Engineering & Sciences, 136(1), 403–419.

cc This work is licensed under a Creative Commons Attribution 4.0 International License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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