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The Importance of Character-Level Information in an Event Detection Model

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Natural Language Processing and Information Systems (NLDB 2021)

Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 12801))

Abstract

This paper tackles the task of event detection that aims at identifying and categorizing event mentions in texts. One of the difficulties of this task is the problem of event mentions corresponding to misspelled, custom, or out-of-vocabulary words. To analyze the impact of character-level features, we propose to integrate character embeddings, that can capture morphological and shape information about words, to a convolutional model for event detection. More precisely, we evaluate two strategies for performing such integration and show that a late fusion approach outperforms both an early fusion approach and models integrating character or subword information such as ELMo or BERT.

This work was partly supported by the European Union’s Horizon 2020 research and innovation program under grants 770299 (NewsEye) and 825153 (Embeddia).

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Notes

  1. 1.

    https://catalog.ldc.upenn.edu/ldc2006t06.

References

  1. Bronstein, O., Dagan, I., Li, Q., Ji, H., Frank, A.: Seed-based event trigger labeling: how far can event descriptions get us? In: ACL-IJCNLP, pp. 372–376 (2015)

    Google Scholar 

  2. Chen, Y., Xu, L., Liu, K., Zeng, D., Zhao, J.: Event extraction via dynamic multi-pooling convolutional neural networks. In: ACL-IJCNLP 2015, pp. 167–176 (2015)

    Google Scholar 

  3. Devlin, J., Chang, M.W., Lee, K., Toutanova, K.: BERT: pre-training of deep bidirectional transformers for language understanding. In: NAACL-HLT 2019, pp. 4171–4186 (2019)

    Google Scholar 

  4. Dos Santos, C., Guimarães, V.: Boosting named entity recognition with neural character embeddings. In: Fifth Named Entity Workshop, pp. 25–33 (2015)

    Google Scholar 

  5. Dos Santos, C., Zadrozny, B.: Learning character-level representations for part-of-speech tagging. In: 31st International Conference on Machine Learning (ICML-14), pp. 1818–1826 (2014)

    Google Scholar 

  6. Dos Santos, C.N., Gatti, M.: Deep convolutional neural networks for sentiment analysis of short texts. In: COLING, pp. 69–78 (2014)

    Google Scholar 

  7. Du, X., Cardie, C.: Event extraction by answering (almost) natural questions. In: 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 671–683 (2020)

    Google Scholar 

  8. Duan, S., He, R., Zhao, W.: Exploiting document level information to improve event detection via recurrent neural networks. In: Eighth International Joint Conference on Natural Language Processing (IJCNLP 2017), pp. 352–361 (2017)

    Google Scholar 

  9. Feng, X., Huang, L., Tang, D., Ji, H., Qin, B., Liu, T.: A language-independent neural network for event detection. In: 54th Annual Meeting of the Association for Computational Linguistics, pp. 66–71 (2016)

    Google Scholar 

  10. Hong, Y., Zhou, W., Zhang, J., Zhou, G., Zhu, Q.: Self-regulation: employing a generative adversarial network to improve event detection. In: 56th Annual Meeting of the Association for Computational Linguistics, pp. 515–526 (2018)

    Google Scholar 

  11. Ji, H., Grishman, R., et al.: Refining event extraction through cross-document inference. In: ACL, pp. 254–262 (2008)

    Google Scholar 

  12. Joulin, A., Grave, E., Bojanowski, P., Mikolov, T.: Bag of tricks for efficient text classification. In: 15th Conference of the European Chapter of the Association for Computational Linguistics (EACL 2017), pp. 427–431 (2017)

    Google Scholar 

  13. Jozefowicz, R., Vinyals, O., Schuster, M., Shazeer, N., Wu, Y.: Exploring the limits of language modeling. arXiv preprint arXiv:1602.02410 (2016)

  14. Kanaris, I., Kanaris, K., Houvardas, I., Stamatatos, E.: Words versus character n-grams for anti-spam filtering. Int. J. Artif. Intell. Tools 16(06), 1047–1067 (2007)

    Article  Google Scholar 

  15. Kim, Y., Jernite, Y., Sontag, D., Rush, A.M.: Character-aware neural language models. In: Thirtieth AAAI Conference on Artificial Intelligence, pp. 2741–2749 (2016)

    Google Scholar 

  16. Lample, G., Ballesteros, M., Subramanian, S., Kawakami, K., Dyer, C.: Neural architectures for named entity recognition. In: NAACL-HLT 2016, pp. 260–270 (2016)

    Google Scholar 

  17. Li, Q., Ji, H., Huang, L.: Joint event extraction via structured prediction with global features. In: ACL, pp. 73–82 (2013)

    Google Scholar 

  18. Li, W., Cheng, D., He, L., Wang, Y., Jin, X.: Joint event extraction based on hierarchical event schemas from FrameNet. IEEE Access 7, 25001–25015 (2019)

    Article  Google Scholar 

  19. Liu, J., Chen, Y., Liu, K., Zhao, J.: Event detection via gated multilingual attention mechanism. In: Thirty-second AAAI Conference on Artificial Intelligence (AAAI-18) (2018)

    Google Scholar 

  20. Liu, S., Chen, Y., Liu, K., Zhao, J.: Exploiting argument information to improve event detection via supervised attention mechanisms. In: 55th Annual Meeting of the Association for Computational Linguistics (ACL 2017), pp. 1789–1798 (2017)

    Google Scholar 

  21. Luong, M.T., Manning, C.D.: Achieving open vocabulary neural machine translation with hybrid word-character models. In: 54th Annual Meeting of the Association for Computational Linguistics (ACL 2016), pp. 1054–1063 (2016)

    Google Scholar 

  22. Ma, X., Hovy, E.: End-to-end sequence labeling via bi-directional LSTM-CNNs-CRF. In: 54th Annual Meeting of the Association for Computational Linguistics, pp. 1064–1074 (2016)

    Google Scholar 

  23. Mikolov, T., Chen, K., Corrado, G., Dean, J.: Efficient estimation of word representations in vector space. In: International Conference on Learning Representations (ICLR 2013), Workshop Track (2013)

    Google Scholar 

  24. Muller, B., Sagot, B., Seddah, D.: Enhancing BERT for lexical normalization. In: 5th Workshop on Noisy User-generated Text (W-NUT 2019), pp. 297–306 (2019)

    Google Scholar 

  25. Nguyen, T.H., Cho, K., Grishman, R.: Joint event extraction via recurrent neural networks. In: NAACL-HLT, pp. 300–309 (2016)

    Google Scholar 

  26. Nguyen, T.H., Fu, L., Cho, K., Grishman, R.: A two-stage approach for extending event detection to new types via neural networks. In: ACL 2016, p. 158 (2016)

    Google Scholar 

  27. Nguyen, T.H., Grishman, R.: Event detection and domain adaptation with convolutional neural networks. In: ACL-IJCNLP 2015, pp. 365–371 (2015)

    Google Scholar 

  28. Nguyen, T.H., Grishman, R.: Modeling skip-grams for event detection with convolutional neural networks. In: EMNLP (2016)

    Google Scholar 

  29. Nguyen, T.H., Grishman, R.: Graph convolutional networks with argument-aware pooling for event detection. In: Thirty-Second AAAI Conference on Artificial Intelligence (AAAI 2018) (2018)

    Google Scholar 

  30. Pennington, J., Socher, R., Manning, C.D.: GloVe: global vectors for word representation. In: EMNLP, pp. 1532–1543 (2014)

    Google Scholar 

  31. Peters, M., et al.: Deep contextualized word representations. In: NAACL-HLT 2018, pp. 2227–2237 (2018)

    Google Scholar 

  32. Sun, L., et al.: Adv-BERT: BERT is not robust on misspellings! Generating nature adversarial samples on BERT. arXiv preprint arXiv:2003.04985 (2020)

  33. Wadden, D., Wennberg, U., Luan, Y., Hajishirzi, H.: Entity, relation, and event extraction with contextualized span representations. In: EMNLP-IJCNLP 2019, pp. 5784–5789 (2019)

    Google Scholar 

  34. Wang, X., Han, X., Liu, Z., Sun, M., Li, P.: Adversarial training for weakly supervised event detection. In: NAACL-HLT 2019, pp. 998–1008 (2019)

    Google Scholar 

  35. Yang, S., Feng, D., Qiao, L., Kan, Z., Li, D.: Exploring pre-trained language models for event extraction and generation. In: 57th Annual Meeting of the Association for Computational Linguistics, pp. 5284–5294 (2019)

    Google Scholar 

  36. Zhang, T., Ji, H., Sil, A.: Joint entity and event extraction with generative adversarial imitation learning. Data Intell. 1(2), 99–120 (2019)

    Article  Google Scholar 

  37. Zhang, X., Zhao, J., LeCun, Y.: Character-level convolutional networks for text classification. In: Advances in Neural Information Processing Systems, pp. 649–657 (2015)

    Google Scholar 

  38. Zhao, Y., Jin, X., Wang, Y., Cheng, X.: Document embedding enhanced event detection with hierarchical and supervised attention. In: 56th Annual Meeting of the Association for Computational Linguistics (ACL 2018), pp. 414–419 (2018)

    Google Scholar 

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Author information

Authors and Affiliations

  1. University of La Rochelle, L3i, 17000, La Rochelle, France

    Emanuela Boros

  2. Université Paris-Saclay, CEA, List, 91120, Palaiseau, France

    Romaric Besançon & Olivier Ferret

  3. Université Paris-Saclay, CNRS, LIMSI, ENSIIE, 91405, Orsay, France

    Brigitte Grau

Authors
  1. Emanuela Boros
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  2. Romaric Besançon
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  3. Olivier Ferret
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  4. Brigitte Grau
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Corresponding author

Correspondence to Emanuela Boros .

Editor information

Editors and Affiliations

  1. Conservatoire National des Arts et Métiers, Paris, France

    Elisabeth Métais

  2. University of Derby, Derby, UK

    Farid Meziane

  3. German Research Center for Artificial Intelligence, Saarbrücken, Germany

    Helmut Horacek

  4. University of Hertfordshire, Hatfield, UK

    Epaminondas Kapetanios

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Boros, E., Besançon, R., Ferret, O., Grau, B. (2021). The Importance of Character-Level Information in an Event Detection Model. In: Métais, E., Meziane, F., Horacek, H., Kapetanios, E. (eds) Natural Language Processing and Information Systems. NLDB 2021. Lecture Notes in Computer Science(), vol 12801. Springer, Cham. https://doi.org/10.1007/978-3-030-80599-9_11

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  • DOI: https://doi.org/10.1007/978-3-030-80599-9_11

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-80598-2

  • Online ISBN: 978-3-030-80599-9

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