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

作　　者：Bin He Hao Meng Zhejin Zhang Rui Liu Ting Zhang 

机构地区：[1]Faculty of Artificial Intelligence in Education,Central China Normal University,Wuhan,430079,China

出　　处：《Computer Modeling in Engineering & Sciences》2023年第7期403-419,共17页工程与科学中的计算机建模（英文）

基　　金：supported by the National Natural Science Foundation of China (Nos.62177024,62007014);the Humanities and Social Sciences Youth Fund of the Ministry of Education (No.20YJC880024);China Post Doctoral Science Foundation (No.2019M652678);the Fundamental Research Funds for the Central Universities (No.CCNU20ZT019).

摘　　要：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 proposedmethod 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.

关 键 词：Quantity relation extraction algebra story problem solving qualia role entity dependency graph 

分 类 号：O15[理学—数学]