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Article summary:
1. A large-scale, high-quality training corpus is built in a fully automated way to address the Open Relation Extraction (ORE) task.
2. A tagging scheme is proposed to transform the ORE task into a sequence tagging processing.
3. A hybrid neural network model (HNN4ORT) is proposed for open relation tagging, which employs Ordered Neurons LSTM and Dual Aware Mechanism to capture associations among arguments and relations.
Article analysis:
The article provides an overview of the Hybrid Neural Network Model (HNN4ORT) for Open Relation Extraction (ORE). The authors have done a good job of providing evidence for their claims, such as citing previous research and experiments that demonstrate the effectiveness of their model. However, there are some areas where more information could be provided. For example, the authors do not provide any information on potential risks associated with using this model or any potential biases that may arise from its use. Additionally, they do not discuss any counterarguments or alternative approaches that could be used instead of HNN4ORT. Furthermore, while the authors provide evidence for their claims, they do not explore any unexplored counterarguments or present both sides equally when discussing their findings. Finally, it is unclear if the authors have taken into account any possible promotional content in their paper as they do not mention it explicitly.