面向方面级情感分类的特征融合学习网络

在方面级情感分类任务中,现有方法强化方面词信息能力较弱,局部特征信息利用不充分.针对上述问题,文中提出面向方面级情感分类的特征融合学习网络.首先,将评论处理为文本、方面和文本-方面的输入序列,通过双向Transformer的表征编码器得到输入的向量表示后,使用注意力编码器进行上下文和方面词的建模,获取隐藏状态,提取语义信息.然后,基于隐藏状态特征,采用方面转换组件生成方面级特定的文本向量表示,将方面信息融入上下文表示中.最后,对于方面级特定的文本向量通过文本位置加权模块提取局部特征后,与全局特征进行融合学习,得到最终的表示特征,并进行情感分类.在英文数据集和中文评论数据集上的实验表明,文中网络提升分类效果.

Feature Fusion Learning Network for Aspect-Level Sentiment Classification

In the aspect-level sentiment classification task, the abilities of the existing methods to enhance aspect terms information and utilize local feature information are weak. To settle this problem, a feature fusion learning network(FFLN) is proposed. Firstly, comments are processed into text, aspect and text-aspect as input. After obtaining vector representation of the input by bidirectional encoder representation from the transformers model, the attention encoder is utilized to obtain the hidden state of the context and aspect items and extract the semantic information. Then, based on the hidden state feature, aspect-specific text vector representation is generated using aspect-specific transformation component to integrate aspect terms information into context representation. Finally, the local features are extracted from aspect-specific text vector by the context position weighted module. The final representation features are obtained by the fusion learning of global and local features, and sentiment classification is conducted. Experiments on classical English datasets and Chinese review datasets show that FFLN improves the classification effect.