Integrating element correlation with prompt-based spatial relation extraction

Abstract

Spatial relations in text refer to how a geographical entity is located in space in relation to a reference entity. Extracting spatial relations from text is a fundamental task in natural language understanding. Previous studies have only focused on generic fine-tuning methods with additional classifiers, ignoring the importance of the semantic correlation between different spatial elements and the large offset between the relation extraction task and the pre-trained models. To address the above two issues, we propose a spatial relation extraction model based on Dual-view Prompt and Element Correlation (DPEC). Specifically, we first reformulate spatial relation extraction as a mask language model with a Dual-view Prompt (i.e., Link Prompt and Confidence Prompt). Link Prompt can not only guide the model to incorporate more contextual information related to the spatial relation extraction task, but also better adapt to the original pre-training task of the language models. Meanwhile, Confidence Prompt can measure the confidence of candidate triplets in Link Prompt and work as a supplement to identify those easily confused examples in Link Prompt. Moreover, we incorporate the element correlation to measure the consistency between different spatial elements, which is an effective cue for identifying the rationality of spatial relations. Experimental results on the popular SpaceEval show that our DPEC significantly outperforms the SOTA baselines.

Graphical abstract

Keywords

spatial relation extraction / Dual-view Prompt / spatial element correlation / Link Prompt / Confidence Prompt

Feng Wang received the BS degree from Shanxi University, China in 2021. He is now a MS student in the School of Computer Science and Technology at Soochow University, China. His research interest lies in information extraction

Sheng Xu received the MS degree from Soochow University, China in 2019. He is now a PhD student in the School of Computer Science and Technology at Soochow University, China. His research interest lies in discourse relation recognition and event relation extraction

Peifeng Li received his PhD degree in Computer Science from Soochow University, China in 2006. He has been a professor in the School of Computer Science and Technology at Soochow University, China since 2015. His research interests include Chinese computing, information extraction

Qiaoming Zhu received his PhD degree in Computer Science from Soochow University, China in 2006. He is now a professor in the School of Computer Science and Technology at Soochow University, China. His research interests include Chinese computing, discourse analysis

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Acknowledgements

The authors would like to thank the anonymous reviewers for their comments on this paper. This work was supported by the National Natural Science Foundation of China (Grant Nos. 62276177 and 62376181).

Competing interests

The authors declare that they have no competing interests or financial conflicts to disclose.

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