Advertisement

Science China Information Sciences

, 61:092106 | Cite as

A language-independent neural network for event detection

  • Xiaocheng Feng
  • Bing Qin
  • Ting Liu
Research Paper

Abstract

Event detection remains a challenge because of the difficulty of encoding the word semantics in various contexts. Previous approaches have heavily depended on language-specific knowledge and preexisting natural language processing tools. However, not all languages have such resources and tools available compared with English language. A more promising approach is to automatically learn effective features from data, without relying on language-specific resources. In this study, we develop a language-independent neural network to capture both sequence and chunk information from specific contexts and use them to train an event detector for multiple languages without any manually encoded features. Experiments show that our approach can achieve robust, efficient and accurate results for various languages. In the ACE 2005 English event detection task, our approach achieved a 73.4% F-score with an average of 3.0% absolute improvement compared with state-of-the-art. Additionally, our experimental results are competitive for Chinese and Spanish.

Keywords

nature language processing event detection neural networks representation learning 

Notes

Acknowledgements

This work was supported by National Natural Science Foundation of China (Grant Nos. 61632011, 61772156, 61702137).

References

  1. 1.
    Jurafsky D, Martin J H. Speech & Language Processing. London: Pearson Education India, 2000Google Scholar
  2. 2.
    Manning C D. Foundations of Statistical Natural Language Processing. Cambridge: MIT Press, 1999MATHGoogle Scholar
  3. 3.
    Gao Y, Zhang H W, Zhao X B, et al. Event classification in microblogs via social tracking. ACM Trans Intel Syst Technol, 2017, 8: 35Google Scholar
  4. 4.
    Zhao S C, Gao Y, Ding G G, et al. Real-time multimedia social event detection in microblog. IEEE Trans Cybern, 2017. doi: 10.1109/TCYB.2017.2762344Google Scholar
  5. 5.
    Nguyen T H, Grishman R. Event detection and domain adaptation with convolutional neural networks. In: Proceedings of the 53rd Annual Meeting of the Association for Computational Linguistics, Beijing, 2015. 365–371Google Scholar
  6. 6.
    Peng H R, Song Y Q, Roth D. Event detection and co-reference with minimal supervision. In: Proceedings of the 2016 Conference on Empirical Methods in Natural Language Processing, Austin, 2016. 392–402CrossRefGoogle Scholar
  7. 7.
    Wang Z Q, Zhang Y. A neural model for joint event detection and summarization. In: Proceedings of Conference on Empirical Methods in Natural Language Processing, Copenhagen, 2017CrossRefGoogle Scholar
  8. 8.
    Hong Y, Zhang J F, Ma B, et al. Using cross-entity inference to improve event extraction. In: Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies, Portland, 2011Google Scholar
  9. 9.
    Ji H, Grishman R. Refining event extraction through cross-document inference. In: Proceedings of the Annual Meeting of the Association for Computational Linguistics (ACL) with the Human Language Technology Conference, Columbus, 2008. 254–262Google Scholar
  10. 10.
    Li J W, Luong M, Jurafsky D. A hierarchical neural autoencoder for paragraphs and documents. 2015. ArXiv:1506.01057CrossRefGoogle Scholar
  11. 11.
    Li Q, Ji H, Huang L. Joint event extraction via structured prediction with global features. In: Proceedings of the 51st Annual Meeting of the Association for Computational Linguistics, Sofia, 2013. 73–82Google Scholar
  12. 12.
    Liao S S, Grishman R. Using document level cross-event inference to improve event extraction. In: Proceedings of the 48th Annual Meeting of the Association for Computational Linguistics, Uppsala, 2010. 789–797Google Scholar
  13. 13.
    Dzmitry B, Kyunghyun C, Yoshua B. Neural machine translation by jointly learning to align and translate. 2014. ArXiv:1409.0473Google Scholar
  14. 14.
    Feng X C, Tang D Y, Qin B, et al. English-Chinese Knowledge base Translation with Neural Network. In: Proceedins of the 26th International Conference on Computational Linguistics: Technical Papers, Osaka, 2016. 2935–2944Google Scholar
  15. 15.
    Feng X C, Guo J, Qin B, et al. Effective deep memory networks for distant supervised relation extraction. In: Proceeding of the 26th International Joint Conference on Artificial Intelligence, Melbourne, 2017. 4002–4008Google Scholar
  16. 16.
    Zeng D J, Liu K, Lai S W, et al. Relation classification via convolutional deep neural network. In: Proceedings of the 25th International Conference on Computational Linguistics, Dublin, 2014. 2335–2344Google Scholar
  17. 17.
    Tang D Y, Qin B, Liu T. Document modeling with gated recurrent neural network for sentiment classification. In: Proceedings of Conference on Empirical Methods in Natural Language Processing, Lisbon, 2015. 1422–1432Google Scholar
  18. 18.
    Harris Z S. Distributional structure. Word, 1954, 10: 146–162CrossRefGoogle Scholar
  19. 19.
    Feng X C, Huang L F, Tang D Y, et al. A language-independent neural network for event detection. In: Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics, Berlin, 2016. 66–71Google Scholar
  20. 20.
    Chen Y B, Xu L H, Liu K, et al. Event extraction via dynamic multi-pooling convolutional neural networks. In: Proceedings of the 53rd Annual Meeting of the Association for Computational Linguistics and the 7th International Joint Conference on Natural Language Processing, Beijing, 2015. 167–176Google Scholar
  21. 21.
    David A. The stages of event extraction. In: Proceedings of the Workshop on Annotating and Reasoning about Time and Events, Sydney, 2006Google Scholar
  22. 22.
    Li Q, Ji H. Incremental joint extraction of entity mentions and relations. In: Proceedings of the Association for Computational Linguistics, Baltimore, 2014. 402–412Google Scholar
  23. 23.
    McClosky D, Surdeanu M, Manning C D. Event extraction as dependency parsing. In: Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies, Portland, 2011. 1626–1635Google Scholar
  24. 24.
    Goyal K, Jauhar S K, Li H Y, et al. A structured distributional semantic model for event co-reference. In: Proceedings of the 51st Annual Meeting of the Association for Computational Linguistics, Sofia, 2013. 467–473Google Scholar
  25. 25.
    Li J W, Jurafsky D, Hovy E. When are tree structures necessary for deep learning of representations? 2015. ArXiv:1503.00185CrossRefGoogle Scholar
  26. 26.
    Graves A. Supervised Sequence Labelling with Recurrent Neural Networks. Berlin: Springer, 2012CrossRefMATHGoogle Scholar
  27. 27.
    Cao K, Li X, Fan M, et al. Improving event detection with active learning. In: Proceedings of Recent Advances in Natural Language Processing, Hissar, 2015. 72–77Google Scholar
  28. 28.
    Baroni M, Dinu G, Georgiana K. Dont count, predict! a systematic comparison of context-counting vs. contextpredicting semantic vectors. In: Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics, Baltimore, 2014. 238–247Google Scholar
  29. 29.
    Mikolov T, Sutskever I, Chen K, et al. Distributed representations of words and phrases and their compositionality. In: Proceedings of the 27th Annual Conference on Neural Information Processing Systems, Lake Tahoe, 2013Google Scholar
  30. 30.
    Hochreiter S, Schmidhuber J. LSTM can solve hard long time lag problems. In: Proceedings of Conference on Neural Information Processing Systems, Denver, 1997. 473–479Google Scholar
  31. 31.
    Zaremba W, Sutskever I, Vinyals O. Recurrent neural network regularization. 2014. ArXiv:1409.2329Google Scholar
  32. 32.
    Liu Y, Wei F R, Li S J, et al. A dependency-based neural network for relation classification. 2015. ArXiv:1507.04646CrossRefGoogle Scholar
  33. 33.
    Zeiler M D. ADADELTA: an adaptive learning rate method. 2012. ArXiv:1212.5701Google Scholar
  34. 34.
    Chen C, Ng V. Joint modeling for chinese event extraction with rich linguistic features. Citeseer, 2012, 290: 529–544Google Scholar
  35. 35.
    Chen Z, Ji H. Language specific issue and feature exploration in Chinese event extraction. In: Proceedings of Annual Conference of the North American Chapter of the Association for Computational Linguistics, Boulder, 2009. 209–212Google Scholar
  36. 36.
    Liu S L, Liu K, He S Z, et al. A probabilistic soft logic based approach to exploiting latent and global information in event classification. In: Proceedings of the 30th AAAI Conference on Artificial Intelligence, Phoenix, 2016. 2993–2999Google Scholar
  37. 37.
    Liu S L, Chen Y B, He S Z, et al. Leveraging framenet to improve automatic event detection. In: Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics, Berlin, 2016. 2134–2143Google Scholar
  38. 38.
    Liu S L, Chen Y B, Liu K, et al. Exploiting argument information to improve event detection via supervised attention mechanisms. In: Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics, Vancouver, 2017. 1789–1798Google Scholar
  39. 39.
    Liu T, Che W X, Li Z H. Language technology platform. J Chinese Inf Proc, 2011, 25: 53–62Google Scholar
  40. 40.
    Tanev H, Zavarella V, Linge J, et al. Exploiting machine learning techniques to build an event extraction system for portuguese and spanish. Linguam´atica, 2009, 1: 55–66Google Scholar

Copyright information

© Science China Press and Springer-Verlag GmbH Germany, part of Springer Nature 2018

Authors and Affiliations

  1. 1.Computer Science and TechnologyHarbin Institute of TechnologyHarbinChina

Personalised recommendations