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. 2022 Mar 7;15:739535. doi: 10.3389/fnins.2021.739535

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Copyright © 2022 Lin, Xu, Sheng, Chen and Chen.

This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

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The AT-NeuroEAE model. The text vectorization layer encodes sentences as textual vectors based on lexical units, case features, and domain terminology dictionaries. The event element prediction layer predicts the potential event elements by using the BiLSTM-CRF model. The role-attribute recognition layer identifies the role and attribute of argument by using the sigmoid function. The adversarial learning mechanism adds small and persistent disturbances to the input of joint model for improving the robustness and generalization of the model. BiLSTM: bi-directional long short-term memory; CRF: conditional random fields; adv: adversarial learning.