基于自注意力的门控卷积神经网络的要素类情感分类研究

Research on Aspect Category Sentiment Classification Based on Gated Convolution Neural Network Combined with Self-Attention Mechanism

摘要: [目的/意义]近年来，将注意力机制与LSTM模型结合的方法常被用于要素类情感分类任务，但是该方法存在参数多、训练时间长的弊端。门控卷积神经网络模型不仅结构简单、参数少、运行时间短，还能分别提取要素特征和情感特征、具有较高的分类精度，但是该模型采用的要素类嵌入是预定义的、与上下文无关。对要素类情感分类任务来说，要素类的质量对预测文本在要素类上情感极性的准确率的重要性不言而喻。[方法/过程]本文关联要素类提取和要素类情感分类任务，提出融合自注意力机制下的要素类特征的门控卷积神经网络模型，通过结合自注意力机制的神经网络提取出基于上下文优化的要素类嵌入，然后将优化后的要素类向量和文本词向量通过门控卷积神经网络进行训练。[结果/结论]在2014年至2016年的SemEval数据集上的实验结果表明，本文提出的模型能有效改善要素类提取的效果和提高要素类情感分类的分类准确率。

Abstract: [Purpose/Significance] In recent years, a common method for aspect category sentiment classification is to combine LSTM model with attention mechanism. Compared to that, the gated convolutional neural network model not only has a simple structure, fewer parameters and shorter training time, but also achieves higher classification accuracy being able to extract aspect features and emotion features. [Method/Process] Considering that the quality of aspect category is crucial for aspect category sentiment classification, this paper coupled aspect category extraction and aspect category sentiment classification, and put forward Gated Convolutional Neural Network with Self Attention-based Aspect Embedding (GCAE_SelfAtt) model to relate the aspect category embeddings to corresponding context, and to achieve a higher accuracy. [Result/Conclusion] The experiment on SemEval dataset shows that GCAE_SelfAtt model does help to extract more coherent aspect categories and achieve higher accuracy for sentiment classification.