We investigate the task of open domain opinion relation extraction. Given a large number of unlabelled texts, we propose an efficient distantly supervised framework based on pattern matching and neural net work classifiers. The patterns are de signed to automatically generate training data, and the deep learning model is de signed to capture various lexical and syn tactic features. The result algorithm is fast and scalable on large-scale corpus. We test the system on the Amazon online review dataset, and show that the proposed model is able to achieve promising performances without any human annotations.
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