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        <title>A Hybrid Model of Classification and Generation for Spatial Relation Extraction</title>
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        <namePart type="given">Feng</namePart>
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    <name type="personal">
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    <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.</abstract>
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        <url>https://aclanthology.org/2022.coling-1.166</url>
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    <part>
        <date>2022-10</date>
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            <start>1915</start>
            <end>1924</end>
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%0 Conference Proceedings
%T A Hybrid Model of Classification and Generation for Spatial Relation Extraction
%A Wang, Feng
%A Li, Peifeng
%A Zhu, Qiaoming
%S Proceedings of the 29th International Conference on Computational Linguistics
%D 2022
%8 October
%I International Committee on Computational Linguistics
%C Gyeongju, Republic of Korea
%F wang-etal-2022-hybrid
%X 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.
%U https://aclanthology.org/2022.coling-1.166
%P 1915-1924

Markdown (Informal)

[A Hybrid Model of Classification and Generation for Spatial Relation Extraction](https://aclanthology.org/2022.coling-1.166) (Wang et al., COLING 2022)

• A Hybrid Model of Classification and Generation for Spatial Relation Extraction (Wang et al., COLING 2022)

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.