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Extracting spatial relations from texts is a fundamental task for naturallanguage understanding and previous studies only regard it as a classificationtask, ignoring those spatial relations with null roles due to their poorinformation. To address the above issue, we first view spatial relationextraction as a generation task and propose a novel hybrid model HMCGR for thistask. HMCGR contains a generation and a classification model, while the formercan generate those null-role relations and the latter can extract thosenon-null-role relations to complement each other. Moreover, a reflexivityevaluation mechanism is applied to further improve the accuracy based on thereflexivity principle of spatial relation. Experimental results on SpaceEvalshow that HMCGR outperforms the SOTA baselines significantly.

A Hybrid Model of Classification and Generation for Spatial Relation Extraction