使用深度长短时记忆模型对于评价词和评价对象的联合抽取

Joint Extraction of Opinion Targets and Opinion Words Based on LSTM

评价词和评价对象抽取在意见挖掘中是一个重要的任务,我们在句子级评价词和评价对象联合抽取任务上研究了长短时记忆(long short-term memory)神经网络模型的几种变种应用。长短时记忆神经网络模型是一种循环神经网络模型,该模型使用长短时记忆模型单元作为循环神经网络的记忆单元,它能够获得更多的长距离上下文信息,同时避免了普通循环神经网络的梯度消失和梯度爆炸的问题。我们对比了传统的方法,实验结果证明长短时记忆神经网络模型优于以前的方法,在细粒度评价词和评价对象的联合抽取中达到更好的性能。

To deal with opinion word and opinion target extraction,we explore several variants of long short-term memory recurrent neural networks for joint extraction of them at sentence-level.We also compare our models with previous classical approaches.The results of the experiments show that long short-term memory recurrent neural networks outperform previous baselines,achieving new state-of-the-art results for joint extraction of fine-grained opinion words and opinion targets.