Open Access

ARTICLE

Solving Arithmetic Word Problems of Entailing Deep Implicit Relations by Qualia Syntax-Semantic Model

Hao Meng, Xinguo Yu^*, Bin He, Litian Huang, Liang Xue, Zongyou Qiu

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

* Corresponding Author: Xinguo Yu. Email:

(This article belongs to the Special Issue: Cognitive Computing and Systems in Education and Research)

Computers, Materials & Continua 2023, 77(1), 541-555. https://doi.org/10.32604/cmc.2023.041508

Received 25 April 2023; Accepted 25 July 2023; Issue published 31 October 2023

Abstract

Solving arithmetic word problems that entail deep implicit relations is still a challenging problem. However, significant progress has been made in solving Arithmetic Word Problems (AWP) over the past six decades. This paper proposes to discover deep implicit relations by qualia inference to solve Arithmetic Word Problems entailing Deep Implicit Relations (DIR-AWP), such as entailing commonsense or subject-domain knowledge involved in the problem-solving process. This paper proposes to take three steps to solve DIR-AWPs, in which the first three steps are used to conduct the qualia inference process. The first step uses the prepared set of qualia-quantity models to identify qualia scenes from the explicit relations extracted by the Syntax-Semantic (S²) method from the given problem. The second step adds missing entities and deep implicit relations in order using the identified qualia scenes and the qualia-quantity models, respectively. The third step distills the relations for solving the given problem by pruning the spare branches of the qualia dependency graph of all the acquired relations. The research contributes to the field by presenting a comprehensive approach combining explicit and implicit knowledge to enhance reasoning abilities. The experimental results on Math23K demonstrate hat the proposed algorithm is superior to the baseline algorithms in solving AWPs requiring deep implicit relations.

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