Abstract
Mining causal relation in text is a complex and critical natural language understanding task. Recently, many efforts focus on extracting causal event pairs in text by exploiting sequence labeling. However, few studies give the uniform definition of the annotation scheme and the labeling boundary of causal events. To address these issues, this paper proposes a novel causal event labeling scheme based on dependency syntactic, which can express the complete semantics of causal relation, as well as delineate the causal event boundaries explicitly. In addition, combined with the relative attention and dependency syntactic, we construct a causal event extraction model named DGLSTM-GRAT-CRF. Experimental results indicate that our model achieves better performance compared with state-of-the-art causality extraction models. Besides, we attempt to explore the influence of various additional features on causal extraction.
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References
Akbik, A., Blythe, D., Vollgraf, R.: Contextual string embeddings for sequence labeling. In: Proceedings of the 27th International Conference on Computational Linguistics, Santa Fe, New Mexico, USA, pp. 1638–1649. ACL (2018)
Devlin, J., Chang, M., Lee, K., Toutanova, K.: BERT: pre-training of deep bidirectional transformers for language understanding. In: Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics, Minneapolis, Minnesota, pp. 4171–4186. ACL (2019)
Feng, C., Qi, K., Shi, G., Huang, H.: Causality extraction with GAN. Acta Automatica Sinica 44(5), 811–818 (2018)
Jie, Z., Lu, W.: Dependency-guided LSTM-CRF for named entity recognition. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing, Hong Kong, China, pp. 3862–3872. ACL (2019)
Kingma, D.P., Ba, J.: Adam: a method for stochastic optimization. In: Proceedings of the International Conference on Learning Representations (2015)
Li, P., Mao, K.: Knowledge-oriented convolutional neural network for causal relation extraction from natural language texts. Expert Syst. Appl. 115, 512–523 (2019)
Li, Z., Li, Q., Zou, X., Ren, J.: Causality extraction based on self-attentive BiLSTM-CRF with transferred embeddings. Neurocomputing 423, 207–219 (2021)
Manning, C., Surdeanu, M., Bauer, J., Finkel, J., Bethard, S., McClosky, D.: The Stanford CoreNLP natural language processing toolkit. In: Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics: System Demonstrations, Baltimore, Maryland, pp. 55–60. ACL (2014)
Pennington, J., Socher, R., Manning, C.: GloVe: global vectors for word representation. In: Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing, Doha, Qatar, pp. 1532–1543. ACL (2014)
Peters, M., Neumann, M., Iyyer, M., Gardner, M., Clark, C.: Deep contextualized word representations. In: Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, pp. 2227–2237. ACL (2018)
Radinsky, K., Davidovich, S., Markovitch, S.: Learning causality for news events prediction. In: Proceedings of the 21st International Conference on World Wide Web, New York, pp. 909–918. ACM (2012)
Ramshaw, L., Marcus, M.: Text chunking using transformation-based learning. In: Proceedings of the Third Workshop on Very Large Corpora, pp. 82–94. ACL (1995)
Vaswani, A., et al.: Attention is all you need. arXiv e-prints 1706.03762 (2017)
Velicković, P., Cucurull, G., Casanova, A., Romero, A., Lió, P., Bengio, Y.: Graph attention networks. arXiv e-prints 1710.10903 (2017)
Xu, J., Zuo, W., Liang, S.: Causal relation extraction based on graph attention networks. J. Comput. Res. Dev. 57(1), 159–174 (2020)
Yan, H., Deng, B., Li, X., Qiu, X.: TENER: adapting transformer encoder for named entity recognition. arXiv e-prints 1911.04474 (2019)
Zhang, Y., Qi, P., Manning, C.D.: Graph convolution over pruned dependency trees improves relation extraction. In: Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing, pp. 2205–2215 (2018)
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This research is supported by the National Natural Science Foundation of China (Grant No. 61866029).
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He, C., Yan, R. (2021). Causality Extraction Based on Dependency Syntactic and Relative Attention. In: Mantoro, T., Lee, M., Ayu, M.A., Wong, K.W., Hidayanto, A.N. (eds) Neural Information Processing. ICONIP 2021. Communications in Computer and Information Science, vol 1516. Springer, Cham. https://doi.org/10.1007/978-3-030-92307-5_55
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