Automatic understanding and formalization of natural language geometry problems using syntax-semantics models

作者：Gan, Wenbin; Yu, Xinguo

来源：International Journal of Innovative Computing Information and Control, 2018, 14(1): 83-98.

DOI：10.24507/ijicic.14.01.83

• Automatic understanding
• Formalized geometric propositions
• Geometric attributes
• Geometric relations
• Geometry problems
• Relation extraction
• Semantic understanding
• State-of-the-art methods

Automatic understanding of natural language problems is a long-standing challenge research problem in automatic solving. This paper models the understanding of geometry questions as a problem of relation extraction, instead of as the problem of semantic understanding of natural language;further it discovers that the entities and the geometric attribute pattern of elements can play an important role in relation extraction. Based on these ideas this paper proposes a syntax-semantics (S2) model approach to understand geometry problem, targeting to produce a group of relations to represent the given geometry problem. The formalized geometric relations can then be transformed into the target system-native representations for manipulation to obtain geometric solutions. Experiments conducted on the test problem dataset show that 91.5% of questions can be correctly understood and solved, and the F1score in formalizing these problems is substantially high (0.990). The comparisons also demonstrate that the proposed method can achieve good performance against the state-of-the-art method. Integrating the automatic understanding method with different geometry systems will greatly enhance the efficiency and intelligence in automatic solving.

• 出版日期2018
• 单位华中师范大学