A Hybrid Model of Classification and Generation for Spatial Relation Extraction

Kavli Affiliate: Feng Wang

| First 5 Authors: Feng Wang Peifeng Li, Qiaoming Zhu, , ,

| Summary:

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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