Abstract
Event Detection is an essential task in information extraction. However, most existing studies on event detection are designed for English text. There is still a lack of efficient algorithm for Chinese event detection, which is expected to be greatly improved. Recent work has shown that enhanced text representation, such as introducing glyph information, can significantly improve downstream tasks in natural language processing. In this paper, we propose a novel method for Chinese Event Detection via Bidirectional Glyph-aware Dynamic Fusion Network, called CED-BGFN. We use two representations: glyph-aware information and pre-trained language model. To integrate the heterogeneous representation modules, we propose a creative fusion network Bidirectional Glyph-aware Fusion Network, named BGFN. Considering the dynamic interaction of the two expressions, BGFN adaptively learns the fusion weights for the downstream event detection task. We conduct extensive experiments to investigate the validity of the proposed method on the ACE 2005 Chinese corpus. Results demonstrate that compared with the previous state-of-the-art methods, our approach obtains transcendent performance in both event trigger identification task and classification task, with an increase of 5.48 (7.46%) and 5.03 (7.1%) in F1-score, respectively.
The work was partially supported by the National Key Research & Development Program of China under Grant No. 2018YFB0204300, and the National Natural Science Foundation of China under Grant No. 61806216, 62025208 and 61932001.
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References
Cao, S., Lu, W., Zhou, J., Li, X.: cw2vec: learning Chinese word embeddings with stroke n-gram information. In: AAAI (2018)
Chen, Z., Ji, H.: Language specific issue and feature exploration in Chinese event extraction. In: NAACL (2009)
Collobert, R., Weston, J.: A unified architecture for natural language processing: deep neural networks with multitask learning. In: ICML (2008)
Cui, Y., et al.: Pre-training with whole word masking for Chinese bert. arXiv preprint arXiv:1906.08101 (2019)
Devlin, J., Chang, M.W., Lee, K., Toutanova, K.: Bert: pre-training of deep bidirectional transformers for language understanding. In: NAACL (2019)
Ding, N., Li, Z., Liu, Z., Zheng, H., Lin, Z.: Event detection with trigger-aware lattice neural network. In: EMNLP-IJCNLP (2019)
Dos Santos, C., Zadrozny, B.: Learning character-level representations for part-of-speech tagging. In: ICML (2014)
Feng, X., Qin, B., Liu, T.: A language-independent neural network for event detection. Sci. China Inf. Sci. 61(9), 1–12 (2018). https://doi.org/10.1007/s11432-017-9359-x
Han, Z., Jiang, J., Qiao, L., Dou, Y., Xu, J., Kan, Z.: Accelerating event detection with DGCNN and FPGAS. Electronics 9(10), 1666 (2020)
Kan, Z., Qiao, L., Yang, S., Liu, F., Huang, F.: Event arguments extraction via dilate gated convolutional neural network with enhanced local features. IEEE Access 8, 123483–123491 (2020)
Kiela, D., Wang, C., Cho, K.: Dynamic meta-embeddings for improved sentence representations. In: EMNLP (2018)
Li, P., Zhou, G.: Employing morphological structures and sememes for Chinese event extraction. In: COLING (2012)
Li, Y., Li, W., Sun, F., Li, S.: Component-enhanced Chinese character embeddings. In: EMNLP (2015)
Lin, H., Lu, Y., Han, X., Sun, L.: Nugget proposal networks for Chinese event detection. In: ACL (2018)
Liu, Y., et al.: Roberta: A robustly optimized bert pretraining approach. arXiv preprint arXiv:1907.11692 (2019)
Meng, Y., et al.: Glyce: glyph-vectors for Chinese character representations. In: NIPS (2019)
Nikolov, N.I., Hu, Y., Tan, M.X., Hahnloser, R.H.R.: Character-level Chinese-English translation through ASCII encoding. In: Proceedings of the Third Conference on Machine Translation: Research Papers (2018)
Peters, M.E., et al.: Deep contextualized word representations. In: NAACL (2018)
Qin, B., Zhao, Y., Ding, X., Liu, T., Zhai, G.: Event type recognition based on trigger expansion. Tsinghua Sci. Technol. 15(3), 251–258 (2010)
Radford, A., Narasimhan, K., Salimans, T., Sutskever, I.: Improving language understanding by generative pre-training. Technical report, OpenAI (2018)
Santos, C.N.D., Guimaraes, V.: Boosting named entity recognition with neural character embeddings. In: Proceedings of the Fifth Named Entity Workshop, NEWS@ACL (2015)
Sun, Y., et al.: Ernie: enhanced representation through knowledge integration. arXiv preprint arXiv:1904.09223 (2019)
Wu, Y., Zhang, J.: Chinese event extraction based on attention and semantic features: a bidirectional circular neural network. Future Internet 10(10), 95 (2018)
Xia, Y., Liu, Y.: Chinese event extraction using deepneural network with word embedding. arXiv preprint arXiv:1610.00842 (2016)
Xiangyu, X., Tong, Z., Wei, Y., Jinglei, Z., Rui, X., Shikun, Z.: A hybrid character representation for Chinese event detection. In: IJCNN (2019)
Xie, Y., Hu, Y., Xing, L., Wei, X.: Dynamic task-specific factors for meta-embedding. In: Douligeris, C., Karagiannis, D., Apostolou, D. (eds.) KSEM 2019. LNCS (LNAI), vol. 11776, pp. 63–74. Springer, Cham (2019). https://doi.org/10.1007/978-3-030-29563-9_7
Yang, S., Feng, D., Qiao, L., Kan, Z., Li, D.: Exploring pre-trained language models for event extraction and generation. In: ACL (2019)
Yang, Z., Dai, Z., Yang, Y., Carbonell, J., Salakhutdinov, R.R., Le, Q.V.: Xlnet: generalized autoregressive pretraining for language understanding. In: NIPS (2019)
Zeng, Y., Yang, H., Feng, Y., Wang, Z., Zhao, D.: A convolution BiLSTM neural network model for Chinese event extraction. In: Lin, C.-Y., Xue, N., Zhao, D., Huang, X., Feng, Y. (eds.) ICCPOL/NLPCC -2016. LNCS (LNAI), vol. 10102, pp. 275–287. Springer, Cham (2016). https://doi.org/10.1007/978-3-319-50496-4_23
Zhang, W., Ding, X., Liu, T.: Learning target-dependent sentence representations for chinese event detection. In: Zhang, S., Liu, T.-Y., Li, X., Guo, J., Li, C. (eds.) CCIR 2018. LNCS, vol. 11168, pp. 251–262. Springer, Cham (2018). https://doi.org/10.1007/978-3-030-01012-6_20
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Zhai, Q., Kan, Z., Yang, S., Qiao, L., Liu, F., Li, D. (2021). CED-BGFN: Chinese Event Detection via Bidirectional Glyph-Aware Dynamic Fusion Network. In: Karlapalem, K., et al. Advances in Knowledge Discovery and Data Mining. PAKDD 2021. Lecture Notes in Computer Science(), vol 12713. Springer, Cham. https://doi.org/10.1007/978-3-030-75765-6_24
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