[go: up one dir, main page]
More Web Proxy on the site http://driver.im/ Skip to main content
Log in

Element relational graph-augmented multi-granularity contextualized encoding for document-level event role filler extraction

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

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

Access this article

Subscribe and save

Springer+ Basic
£29.99 /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 includes VAT (United Kingdom)

Instant access to the full article PDF.

References

  1. Du X, Cardie C. Document-level event role filler extraction using multi-granularity contextualized encoding. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics. 2020, 8010–8020

    Chapter  Google Scholar 

  2. McLean V. Fourth message understanding conference (MUC-4). In: Proceedings of Fourth Message Understanding Conference (MUC-4). 1992

    Google Scholar 

  3. Wadden D, Wennberg U, Luan Y, Hajishirzi H. Entity, relation, and event extraction with contextualized span representations. In: Proceedings of 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing. 2019, 5784–5789

    Google Scholar 

  4. Du X, Rush A M, Cardie C. GRIT: Generative role-filler transformers for document-level event entity extraction. In: Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics. 2021, 634–644

    Google Scholar 

  5. Paolini G, Athiwaratkun B, Krone J, Jie M, Achille A, Anubhai R, dos Santos C N, Xiang B, Soatto S. Structured prediction as translation between augmented natural languages. In: Proceedings of the 9th International Conference on Learning Representations. 2021, 1–26

    Google Scholar 

  6. Huang K H, Tang S, Peng N. Document-level entity-based extraction as template generation. In: Proceedings of 2021 Conference on Empirical Methods in Natural Language Processing. 2021, 5257–5269

    Chapter  Google Scholar 

  7. Chen Y, Gantt W, Gu W, Chen T, White A, Van Durme B. Iterative document-level information extraction via imitation learning. In: Proceedings of the 17th Conference of the European Chapter of the Association for Computational Linguistics. 2023, 1858–1874

    Chapter  Google Scholar 

Download references

Acknowledgements

This work was supported by the National Natural Science Foundation of China (Grant Nos. U21B2027, U23A20388, 62266028), the Yunnan Provincial Major Science and Technology Special Plan Projects (202302AD080003, 202202AD080003, 202303AP140008), the Yunnan Fundamental Research Projects (202301AS070047), and the Kunming University of Science and Technology’s “Double First-rate” Construction Joint Project (202201BE070001-021).

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Zhengtao Yu.

Ethics declarations

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

Electronic supplementary material

11704_2024_3701_MOESM1_ESM.pdf

Element relational graph-augmented multi-granularity contextualized encoding for document-level event role filler extraction

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Zhu, E., Yu, Z., Huang, Y. et al. Element relational graph-augmented multi-granularity contextualized encoding for document-level event role filler extraction. Front. Comput. Sci. 19, 192326 (2025). https://doi.org/10.1007/s11704-024-3701-4

Download citation

  • Received:

  • Accepted:

  • Published:

  • DOI: https://doi.org/10.1007/s11704-024-3701-4

Navigation