Discourse Component Recognition via Graph Neural Network in Chinese Student Argumentative Essays
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Publisher
Springer International Publishing
Link
https://link.springer.com/content/pdf/10.1007/978-3-031-10983-6_28
Reference21 articles.
1. Afantenos, S., Peldszus, A., Stede, M.: Comparing decoding mechanisms for parsing argumentative structures. Argument Comput. 9(3), 177–192 (2018)
2. Bao, J., Fan, C., Wu, J., Dang, Y., Du, J., Xu, R.: A neural transition-based model for argumentation mining. In: Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers), pp. 6354–6364 (2021)
3. Chakrabarty, T., Hidey, C., Muresan, S., McKeown, K., Hwang, A.: Ampersand: Argument mining for persuasive online discussions (2020)
4. Daxenberger, J., Eger, S., Habernal, I., Stab, C., Gurevych, I.: What is the essence of a claim? cross-domain claim identification, pp. 2055–2066 (2017)
5. Eger, S., Daxenberger, J., Gurevych, I.: Neural end-to-end learning for computational argumentation mining, pp. 11–22 (2017)
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