Skip to main content

HS\(^2\)N: Heterogeneous Semantics-Syntax Fusion Network for Document-Level Event Factuality Identification

  • Conference paper
  • First Online:
PRICAI 2022: Trends in Artificial Intelligence (PRICAI 2022)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 13630))

Included in the following conference series:

Abstract

Event factuality identification (EFI) aims to assess the veracity degree to which an event mentioned in a document has happened, and both semantic and syntactic features are crucial for this task. Most of the previous studies only focused on sentence-level event factuality, which may lead to conflicts among mentions of a specific event in a document. Existing studies on document-level EFI (DEFI) are still scarce and mainly focus on semantic features. To address the above issues, we propose a novel Heterogeneous Semantics-Syntax-fused Network (HS\(^2\)N) for DEFI, which not only integrates both semantic and syntactic information in an efficient way using Biaffine Attention and differentiated alignment method, but also considers both inter-and-intra sentence interaction. Experimental results on the English and Chinese datasets show that our proposed HS\(^2\)N outperforms the state-of-the-art model.

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

Access this chapter

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

Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Similar content being viewed by others

Notes

  1. 1.

    https://github.com/huggingface/transformers.

  2. 2.

    https://github.com/CPF-NLPR/ULGN4DocEFI.

References

  1. Saurí, R., Pustejovsky, J.: FactBank: a corpus annotated with event factuality. Lang. Resour. Eval. 43(3), 227–268 (2009)

    Article  Google Scholar 

  2. Qazvinian, V., Rosengren, E., Radev, D., Mei, Q.: Rumor has it: dentifying misinformation in microblogs. In: Proceedings of the 2011 Conference on Empirical Methods in Natural Language Processing, pp. 1589–1599. Association for Computational Linguistics, USA (2011)

    Google Scholar 

  3. Bian, T., Xiao, X., Xu, T., et al.: Rumor detection on social media with bi-directional graph convolutional networks. In: Proceedings of the Thirty-Fourth AAAI Conference on Artificial Intelligence, pp. 549–556. AAAI Press, USA (2020)

    Google Scholar 

  4. Tu, K., Chen, C., Hou, C., Yuan, J., Li, J., Yuan, X.: Rumor2Vec: a rumor detection framework with joint text and propagation structure representation learning. Inf. Sci. 560, 137–151 (2021)

    Article  Google Scholar 

  5. Saurí, R., Pustejovsky, J.: Are you sure that this happened? Assessing the factuality degree of events in text. Comput. Linguist. 38(2), 261–299 (2012)

    Article  Google Scholar 

  6. Vroe, S., Guillou, L., Stanojević, M., McKenna, N., Steedman, M.: Modality and negation in event extraction. In: Proceedings of the 4th Workshop on Challenges and Applications of Automated Extraction of Socio-political Events from Text (CASE), pp. 31–42. Association for Computational Linguistics, Online (2021)

    Google Scholar 

  7. Qian, Z., Li, P., Zhu, Q.: A two-step approach for event factuality identification. In: 2015 International Conference on Asian Language Processing (IALP), pp. 103–106. IEEE, China (2015)

    Google Scholar 

  8. Qian, Z., Li, P., Zhang, Y., Zhou, G., Zhu, Q.: Event factuality identification via generative adversarial networks with auxiliary classification. In: Proceedings of the 27th International Joint Conference on Artificial Intelligence, pp. 4293–4300. AAAI Press, Sweden (2018)

    Google Scholar 

  9. Veyseh, A., Nguyen, T., Dou, D.: Graph based neural networks for event factuality prediction using syntactic and semantic structures. In: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pp. 4393–4399. Association for Computational Linguistics, Italy (2019)

    Google Scholar 

  10. Le, D., Nguyen, T.: Does it happen? Multi-hop path structures for event factuality prediction with graph transformer networks. In: Proceedings of the Seventh Workshop on Noisy User-generated Text, pp. 46–55. Association for Computational Linguistics, Online (2021)

    Google Scholar 

  11. Qian, Z., Li, P., Zhu, Q., Zhou, G.: Document-level event factuality identification via adversarial neural network. In: Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, pp. 2799–2809. Association for Computational Linguistics, Minnesota (2019)

    Google Scholar 

  12. Cao, P., Chen, Y., Yang, Y., Liu, K., Zhao, J.: Uncertain local-to-global networks for document-level event factuality identification. In: Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing, pp. 2636–2645. Association for Computational Linguistics, Online and Dominican Republic (2021)

    Google Scholar 

  13. Zhang, Y., Li, P., Zhu, Q.: Document-level event factuality identification method with gated convolution networks. Comput. Sci. 47(3), 5 (2020)

    Google Scholar 

  14. Devlin, J., Chang, M., Lee, K., Toutanova, K.: BERT: pre-training of Ddeep bidirectional transformers for language understanding. In: Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, pp. 4171–4186. Association for Computational Linguistics, Minnesota (2019)

    Google Scholar 

  15. Kipf, N., Welling, M.: Semi-supervised classification with graph convolutional networks. In: Proceedings of the 5th International Conference on Learning Representations, France (2017)

    Google Scholar 

  16. Baly, R., Karadzhov, G., Alexandrov, D., Glass, J., Nakov, P.: Predicting factuality of reporting and bias of news media sources. In: Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing, pp. 3528–3539. Association for Computational Linguistics, Belgium (2018)

    Google Scholar 

  17. Singhal, S., Shah, R., Chakraborty, T., Kumaraguru, P., Satoh, S.: SpotFake: a multi-modal framework for fake news detection. In: 2019 IEEE Fifth International Conference on Multimedia Big Data (BigMM), pp. 39–47. IEEE (2018)

    Google Scholar 

  18. Loshchilov, I., Hutter, F.: Decoupled weight decay regularization. In: Proceedings of the 7th International Conference on Learning Representations (ICLR), pp. OpenReview.net, New Orleans (2018)

    Google Scholar 

  19. Paatero, P., Tapper, U.: Positive matrix factorization: a non-negative factor model with optimal utilization of error estimates of data values. Environmetrics 5(2) (1994)

    Google Scholar 

  20. Dozat, T., Manning, C.: Deep biaffine attention for neural dependency parsing. In: Proceedings of 5th International Conference on Learning Representations (ICLR), pp. OpenReview.net, Toulon (2017)

    Google Scholar 

  21. Yu, J., Bohnet, B., Poesio, M.: Named entity recognition as dependency parsing. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 6470–6476. Association for Computational Linguistics, Online (2020)

    Google Scholar 

  22. Shi, C., Li, Y., Zhang, J., Sun, Y., Philip, S.Y.: A survey of heterogeneous information network analysis. IEEE Trans. Knowl. Data Eng. 29(1), 17–37 (2016)

    Article  Google Scholar 

Download references

Acknowledgements

The authors would like to thank the two anonymous reviewers for their comments on this paper. This research was supported by the National Natural Science Foundation of China (No. 61836007 and 62006167.), and Project Funded by the Priority Academic Program Development of Jiangsu Higher Education Institutions (PAPD).

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Xiaoxu Zhu .

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2022 The Author(s), under exclusive license to Springer Nature Switzerland AG

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Zhang, Z., Liu, C., Qian, Z., Zhu, X., Li, P. (2022). HS\(^2\)N: Heterogeneous Semantics-Syntax Fusion Network for Document-Level Event Factuality Identification. In: Khanna, S., Cao, J., Bai, Q., Xu, G. (eds) PRICAI 2022: Trends in Artificial Intelligence. PRICAI 2022. Lecture Notes in Computer Science, vol 13630. Springer, Cham. https://doi.org/10.1007/978-3-031-20865-2_23

Download citation

  • DOI: https://doi.org/10.1007/978-3-031-20865-2_23

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-20864-5

  • Online ISBN: 978-3-031-20865-2

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics