A Hybrid Model of Classification and Generation for Spatial Relation Extraction

• URL: http://arxiv.org/abs/2208.06961v1
• Date: Mon, 15 Aug 2022 01:31:44 GMT
• Title: A Hybrid Model of Classification and Generation for Spatial Relation Extraction
• Authors: Feng Wang Peifeng Li and Qiaoming Zhu
• Abstract summary: We first view spatial relation extraction as a generation task and propose a novel hybrid model HMCGR for this task. Experimental results on SpaceEval show that HMCGR outperforms the SOTA baselines significantly.
• Score: 10.611528850772869
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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