A Hybrid Model of Classification and Generation for Spatial Relation Extraction

Feng Wang, Peifeng Li, Qiaoming Zhu


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.
Anthology ID:
2022.coling-1.166
Volume:
Proceedings of the 29th International Conference on Computational Linguistics
Month:
October
Year:
2022
Address:
Gyeongju, Republic of Korea
Venue:
COLING
SIG:
Publisher:
International Committee on Computational Linguistics
Note:
Pages:
1915–1924
Language:
URL:
https://aclanthology.org/2022.coling-1.166
DOI:
Bibkey:
Cite (ACL):
Feng Wang, Peifeng Li, and Qiaoming Zhu. 2022. A Hybrid Model of Classification and Generation for Spatial Relation Extraction. In Proceedings of the 29th International Conference on Computational Linguistics, pages 1915–1924, Gyeongju, Republic of Korea. International Committee on Computational Linguistics.
Cite (Informal):
A Hybrid Model of Classification and Generation for Spatial Relation Extraction (Wang et al., COLING 2022)
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PDF:
https://aclanthology.org/2022.coling-1.166.pdf