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首页> 外文期刊>International Journal of Artificial Intelligence Tools: Architectures, Languages, Algorithms >Automatic Understanding and Formalization of Plane Geometry Proving Problems in Natural Language: A Supervised Approach
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Automatic Understanding and Formalization of Plane Geometry Proving Problems in Natural Language: A Supervised Approach

机译：自然语言中平面几何问题的自动理解和形式化：监督方法

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摘要

Automatically understanding natural language problems is a long-standing challenging research problem in automatic solving. This paper models the understanding of geometry problems as a problem of relation extraction, instead of as the problem of semantic understanding of natural language. Then it further proposes a supervised machine learning method to extract geometric relations, targeting to produce a group of relations to represent the given geometry problem. This method identifies the actual geometric relations from the relation candidates using a classifier trained from the labelled examples. The formalized geometric relations can then be transformed into the target system-native representations for manipulation in various tasks. Experiments conducted on the test problem dataset show that the proposed method can extract geometric relations at high F-1 scores. The comparisons also demonstrate that the proposed method can achieve good performance against the baseline methods. Integrating the automatic understanding method with different geometry systems will greatly enhance the efficiency and intelligence in geometry tutoring.
机译：自动理解自然语言问题是自动解决中的长期挑战性的研究问题。本文模拟了几何问题作为关系提取问题的理解，而不是作为对自然语言的语义理解问题。然后，它进一步提出了一种监督机器学习方法来提取几何关系，靶向产生一组关系来表示给定的几何问题。此方法使用从标记示例训练的分类器识别与关系候选的实际几何关系。然后可以将正式的几何关系转换为目标系统本机表示以进行各种任务的操纵。在测试问题数据集上进行的实验表明，该方法可以在高F-1分数下提取几何关系。比较还证明了该方法可以对基线方法实现良好的性能。与不同的几何系统集成自动理解方法将大大提高几何辅导中的效率和智能。

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