融合多层注意力的方面级情感分析模型

Aspect-Level Sentiment Analysis Model Incorporating Multi-layer Attention

方面情感分析旨在分析给定文本中特定方面的情感极性。针对目前的研究方法存在对方面情感注意力引入不足问题,提出了一种融合BERT和多层注意力的方面级情感分类模型(BERTandMulti-LayerAttention,BMLA)。模型首先提取BERT内部多层方面情感注意力信息,将编码后的方面信息与BERT隐藏层表征向量融合设计了多层方面注意力,然后将多层方面注意力与编码输出文本进行级联,进而增强了句子与方面词之间的长依赖关系。在SemEval2014 Task4和AIChallenger 2018数据集上的实验表明,强化目标方面权重并在上下文进行交互对方面情感分类是有效的。

Aspect sentiment analysis aims to analyze the sentiment polarity of a specific aspect in a given text.In order to solve the problem of insufficient introduction of aspect emotional attention in current research methods,this paper proposes an aspect level emotion classification model based on the fusion of BERT and Multi-Layer Attention(BMLA).Firstly,the model extracts the multi-layer aspect emotional attention information from the inner part of BERT,and designs the multi layer aspect attention by fusing the encoded aspect information with the representation vector of the hidden layer of BERT,then cascades the multi-layer aspect attention with the encoded output text,finally enhances the long dependency relationship between sentences and aspect words.Experiments on the SemEval2014 Task4 and the AI Challenger 2018 datasets show that the proposed model is effective to enhance the weight of the target aspect and interact in context for aspect sentiment classification.