论文标题
用于空间关系提取的分类和生成的混合模型
A Hybrid Model of Classification and Generation for Spatial Relation Extraction
论文作者
论文摘要
从文本中提取空间关系是自然语言理解的一项基本任务，以前的研究仅将其视为一项分类任务，由于信息差而忽略了那些具有无效角色的空间关系。为了解决上述问题，我们首先将空间关系提取视为一项生成任务，并为此任务提出了一种新型的混合模型HMCGR。 HMCGR包含一个生成和分类模型，而前者可以生成那些无效的关系，后者可以提取那些非无效关系以相互补充。此外，使用反射性评估机制，以进一步提高基于空间关系的反射性原理的准确性。 SpaceEval的实验结果表明，HMCGR的表现明显优于SOTA基线。
Extracting spatial relations from texts is a fundamental task for natural language understanding and previous studies only regard it as a classification task, ignoring those spatial relations with null roles due to their poor information. To address the above issue, we first view spatial relation extraction as a generation task and propose a novel hybrid model HMCGR for this task. HMCGR contains a generation and a classification model, while the former can generate those null-role relations and the latter can extract those non-null-role relations to complement each other. Moreover, a reflexivity evaluation mechanism is applied to further improve the accuracy based on the reflexivity principle of spatial relation. Experimental results on SpaceEval show that HMCGR outperforms the SOTA baselines significantly.
