Qualia role-based quantity relation extraction for solving algebra story problems

This paper presents a novel approach to solving algebra story problems in Chinese using a qualia role-based entity-dependency graph (EDG). The method addresses the limitations of traditional neural solvers by explicitly modeling quantity relations hidden in the qualia roles of mathematical objects.

Key Contributions

1. Entity-Dependency Graph (EDG): A novel representation that captures quantity relations in algebra story problems
2. Qualia Role Integration: Leverages qualia structure to understand implicit relationships between mathematical entities
3. Algorithm Design: Develops specific algorithms for EDG generation and quantity relation extraction
4. Chinese Language Support: Addresses the unique challenges of solving algebra problems stated in Chinese

Methodology

The proposed approach consists of several key components:

• Problem Text Analysis: Natural language processing to understand the problem statement
• Entity Extraction: Identification of mathematical objects and their properties
• Qualia Role Assignment: Mapping entities to their qualia roles (formal, constitutive, telic, agentive)
• EDG Construction: Building the entity-dependency graph based on qualia relationships
• Quantity Relation Extraction: Deriving mathematical relationships from the EDG
• Solution Generation: Converting extracted relations into mathematical expressions

Experimental Results

The experimental evaluation demonstrates the effectiveness of the proposed method in solving Chinese algebra story problems, showing improvements over traditional end-to-end neural approaches in terms of accuracy and interpretability.

Impact

This work contributes to the field of educational technology by providing a more interpretable and effective approach to automated math problem solving, with potential applications in intelligent tutoring systems and educational AI.