Home / Advanced Search

  • Title/Keywords

  • Author/Affliations

  • Journal

  • Article Type

  • Start Year

  • End Year

Update SearchingClear
  • Articles
  • Online
Search Results (1)
  • Open Access


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

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

    CMES-Computer Modeling in Engineering & Sciences, Vol.136, No.1, pp. 403-419, 2023, DOI:10.32604/cmes.2023.023242

    Abstract 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… More >

Displaying 1-10 on page 1 of 1. Per Page