Skip to main content

Advertisement

Springer Nature Link
Log in

Integrating element correlation with prompt-based spatial relation extraction

  • Research Article
  • Published:
Frontiers of Computer Science Aims and scope Submit manuscript

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.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Subscribe and save

Springer+ Basic
$34.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Similar content being viewed by others

References

  1. Zhang L, Wang R, Zhou J, Yu J, Ling Z, Xiong H. Joint intent detection and entity linking on spatial domain queries. In: Proceedings of Findings of Association for Computational Linguistics: EMNLP 2020. 2020, 4937–4947

    Chapter  Google Scholar 

  2. Yang T Y, Lan A, Narasimhan K. Robust and interpretable grounding of spatial references with relation networks. In: Proceedings of Findings of the Association for Computational Linguistics: EMNLP 2020. 2020, 1908–1923

    Chapter  Google Scholar 

  3. Song C, Lin Y, Guo S, Wan H. Spatial-temporal synchronous graph convolutional networks: a new framework for spatial-temporal network data forecasting. In: Proceedings of the 34th AAAI Conference on Artificial Intelligence. 2020, 914–921

    Google Scholar 

  4. Pustejovsky J, Kordjamshidi P, Moens M, Levine A, Dworman S, Yocum Z. SemEval-2015 task 8: SpaceEval. In: Proceedings of the 9th International Workshop on Semantic Evaluation. 2015, 884–894

    Google Scholar 

  5. Pustejovsky J, Moszkowicz J L, Verhagen M. Using ISO-space for annotating spatial information. In: Proceedings of International Conference on Spatial Information Theory. 2011

    Google Scholar 

  6. Devlin J, Chang M W, Lee K, Toutanova K. BERT: pre-training of deep bidirectional transformers for language understanding. In: Proceedings of 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1. 2019, 4171–4186

    Google Scholar 

  7. Brown T B, Mann B, Ryder N, Subbiah M, Kaplan J, et al. Language models are few-shot learners. In: Proceedings of the 34th International Conference on Neural Information Processing Systems, 2020, 159

    Google Scholar 

  8. Ramrakhiyani N, Palshikar G, Varma V. A simple neural approach to spatial role labelling. In: Proceedings of the 41st European Conference on IR Research on Advances in Information Retrieval. 2019, 102–108

    Google Scholar 

  9. Shin H J, Park J Y, Yuk D B, Lee J S. BERT-based spatial information extraction. In: Proceedings of the 3rd International Workshop on Spatial Language Understanding. 2020, 10–17

    Chapter  Google Scholar 

  10. Shin T, Razeghi Y, Logan IV R L, Wallace E, Singh S. AutoPrompt: eliciting knowledge from language models with automatically generated prompts. In: Proceedings of 2020 Conference on Empirical Methods in Natural Language Processing. 2020, 4222–4235

    Google Scholar 

  11. Schick T, Schütze H. It’s not just size that matters: small language models are also few-shot learners. In: Proceedings of 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies. 2021, 2339–2352

    Google Scholar 

  12. Mani I, Doran C, Harris D, Hitzeman J, Quimby R, Richer J, Wellner B, Mardis S, Clancy S. SpatialML: annotation scheme, resources, and evaluation. Language Resources and Evaluation, 2010, 44(3): 263–280

    Article  Google Scholar 

  13. Kordjamshidi P, van Otterlo M, Moens M F. Spatial role labeling: towards extraction of spatial relations from natural language. ACM Transactions on Speech and Language Processing, 2011, 8(3): 4

    Article  Google Scholar 

  14. Kordjamshidi P, Bethard S, Moens M F. SemEval-2012 task 3: spatial role labeling. In: Proceedings of the 1st Joint Conference on Lexical and Computational Semantics – Volume 1: Proceedings of the Main Conference and the Shared Task, and Volume 2: Proceedings of the 6th International Workshop on Semantic Evaluation. 2012, 365–373

    Google Scholar 

  15. Kolomiyets O, Kordjamshidi P, Moens M F, Bethard S. Semeval-2013 task 3: spatial role labeling. In: Proceedings of the 2nd Joint Conference on Lexical and Computational Semantics (*SEM), Volume 2: Proceedings of the 7th International Workshop on Semantic Evaluation. 2013, 255–262

    Google Scholar 

  16. Nichols E, Botros F. SpRL-CWW: spatial relation classification with independent multi-class models. In: Proceedings of the 9th International Workshop on Semantic Evaluation. 2015, 895–901

    Google Scholar 

  17. Pennington J, Socher R, Manning C. GloVe: global vectors for word representation. In: Proceedings of 2014 Conference on Empirical Methods in Natural Language Processing. 2014, 1532–1543

    Google Scholar 

  18. D’Souza J, Ng V. Sieve-based spatial relation extraction with expanding parse trees. In: Proceedings of 2015 Conference on Empirical Methods in Natural Language Processing. 2015, 758–768

    Chapter  Google Scholar 

  19. Salaberri H, Arregi O, Zapirain B. IXAGroupEHUSpaceEval: (X-space) a WordNet-based approach towards the automatic recognition of spatial information following the ISO-space annotation scheme. In: Proceedings of the 9th International Workshop on Semantic Evaluation. 2015, 856–861

    Google Scholar 

  20. Rahgooy T, Manzoor U, Kordjamshidi P. Visually guided spatial relation extraction from text. In: Proceedings of 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 2. 2018, 788–794

    Google Scholar 

  21. Qiu Q, Xie Z, Ma K, Chen Z, Tao L. Spatially oriented convolutional neural network for spatial relation extraction from natural language texts. Transactions in GIS, 2022, 26(2): 839–866

    Article  Google Scholar 

  22. Blukis V, Paxton C, Fox D, Garg A, Artzi Y. A persistent spatial semantic representation for high-level natural language instruction execution. In: Proceedings of the 5th Conference on Robot Learning. 2022, 706–717

    Google Scholar 

  23. Wu S, He Y. Enriching pre-trained language model with entity information for relation classification. In: Proceedings of the 28th ACM International Conference on Information and Knowledge Management. 2019, 2361–2364

    Chapter  Google Scholar 

  24. Wang F, Li P, Zhu Q. A hybrid model of classification and generation for spatial relation extraction. In: Proceedings of the 29th International Conference on Computational Linguistics. 2022, 1915–1924

    Google Scholar 

  25. Chia Y K, Bing L, Poria S, Si L. RelationPrompt: leveraging prompts to generate synthetic data for zero-shot relation triplet extraction. In: Proceedings of Findings of Association for Computational Linguistics: ACL 2022. 2022, 45–57

    Chapter  Google Scholar 

  26. Han X, Zhao W, Ding N, Liu Z, Sun M. PTR: prompt tuning with rules for text classification. AI Open, 2022, 3: 182–192

    Article  Google Scholar 

Download references

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).

Author information

Authors and Affiliations

  1. School of Computer Science and Technology, Soochow University, Suzhou, 215006, China

    Feng Wang, Sheng Xu, Peifeng Li & Qiaoming Zhu

Authors
  1. Feng Wang
    View author publications

    You can also search for this author inPubMed Google Scholar

  2. Sheng Xu
    View author publications

    You can also search for this author inPubMed Google Scholar

  3. Peifeng Li
    View author publications

    You can also search for this author inPubMed Google Scholar

  4. Qiaoming Zhu
    View author publications

    You can also search for this author inPubMed Google Scholar

Corresponding author

Correspondence to Peifeng Li.

Ethics declarations

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

Additional information

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.

Electronic Supplementary Material

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Wang, F., Xu, S., Li, P. et al. Integrating element correlation with prompt-based spatial relation extraction. Front. Comput. Sci. 19, 192308 (2025). https://doi.org/10.1007/s11704-023-3305-4

Download citation

  • Received:

  • Accepted:

  • Published:

  • DOI: https://doi.org/10.1007/s11704-023-3305-4

Keywords