基于特征双重蒸馏网络的方面级情感分析

目前方面级情感分析方法主要利用注意力机制来实现句子与方面词的交互,然而该机制容易导致方面词与句子中各词的错误搭配,引入额外噪声。针对此问题,该文提出了一种基于特征双重蒸馏网络的方面级情感分析方法。首先利用BiLSTM提取句子中各词的上下文语义特征,并结合基于上下文的方面词嵌入方法,获取方面词的语义特征。进一步地,利用门控机制构建双重蒸馏门,通过初步蒸馏与精细蒸馏两个过程实现句子与方面词的语义特征交互,获取与方面词相关的句子情感语义特征。最终利用Softmax对获取的情感语义特征进行情感分类。在通用的Laptop、Restaurant和Twitter数据集上进行实验,结果表明,该方法的准确率分别达到79.26%、84.53%和75.30%,宏平均F₁值分别达到75.77%、75.63%和73.21%,优于目前主流方法。

Feature Dual Distillation Network for Aspect-Based Sentiment Analysis

Current methods of aspect-based sentiment analysis usually utilize the attention mechanism to fulfill the interaction between sentence and aspect. However, the attention mechanism often results in the mismatches between words of sentence and aspect, which will introduce extraneous noise. To address this issue, this paper proposes a feature dual distillation network for aspect-based sentiment analysis. Firstly, BiLSTM is utilized to extract context semantic features, and a context-based aspect embedding is utilized to obtain the semantic feature of aspect. Moreover, a gate mechanism is employed to construct a dual distillation gate where preliminary distillation and fine distillation processes are utilized to fulfill the interaction between the semantic features of sentence and aspect. Finally, Softmax is utilized to predict the sentiment polarities. On commonly used Laptop, Restaurant and Twitter datasets, the proposed method performs better than the state-of-the-art methods with 79.26%, 84.53% and 75.30% accuracy, and 75.77%, 75.63% and 73.21% Macro-F₁, respectively.