融合匹配长短时记忆网络和语法距离的方面级情感分析模型

针对现阶段方面级情感分析（ABSA）存在的方面词与不相关上下文错误匹配以及缺乏语法层面特征的问题，提出一种融合匹配长短时记忆网络（mLSTM）和语法距离的ABSA模型mLSTM-GCN。首先，逐词计算方面词与上下文的关联性，并将得到的注意力权重与上下文表示融合作为mLSTM的输入，从而得到与方面词关联度更高的上下文表示；然后，引入语法距离以获得与方面词语法关联度更高的上下文，从而获取更多的上下文特征来指导方面词的建模，并通过方面掩盖层得到方面表示；最后，结合位置权重、上下文表示以及方面表示来进行信息交互，从而获取用于情感分析的特征。在Twitter、REST14和LAP14数据集上的实验结果表明，相较于特定方面的图卷积网络（ASGCN）,mLSTM-GCN的准确率分别提升1.32、2.50和1.63个百分点，宏平均F1分别提升2.52、2.19和1.64个百分点。可见，mLSTM-GCN能够有效降低方面词与不相关上下文错误匹配的概率，提升分类效果。

Aspect-based sentiment analysis model integrating match-LSTM network and grammatical distance

Aiming at the problems of the mismatch between aspect words and irrelevant context and the lack of grammatical level features in Aspect-Based Sentiment Analysis （ABSA） at current stage， an improved ABSA model integrating match-Long Short-Term Memory （mLSTM） and grammatical distances was proposed， namely mLSTM-GCN. Firstly， the correlation between the aspect word and the context was calculated word by word， and the obtained attention weight and the context representation were fused as the input of the mLSTM， so that the context representation with higher correlation with the aspect word was obtained. Then， the grammatical distance was introduced to obtain a context which was more grammatically related to the aspect word， so as to obtain more contextual features to guide the modeling of the aspect word， and obtain the aspect representation through the aspect masking layer. Finally， in order to exchange information， location weights， context representations and aspect representations were combined， thereby obtaining the features for sentiment analysis. Experimental results on Twitter， REST14 and LAP14 datasets show that compared with Aspect-Specific Graph Convolutional Network （ASGCN）， mLSTM-GCN has the accuracy improved by 1.32， 2.50 and 1.63 percentage points， respectively， and has the Macro-F1 score improved by 2.52， 2.19 and 1.64 percentage points， respectively. Therefore， mLSTM-GCN can effectively reduce the probability of mismatch between aspect words and irrelevant context， and improve the classification effect.