结合依存句法分析与交互注意力机制的隐式方面提取

隐式方面提取对于提升细粒度情感分析的准确性具有重要意义,然而现有隐式方面提取技术在处理大规模数据时泛化能力不强。为此,提出结合依存句法分析与交互注意力机制的隐式方面提取模型。首先利用预训练语言模型BERT生成文本的初始表征,然后传递给依存句法引导的自注意力层再次处理,再将两次处理的结果经交互注意力机制进一步提取特征,最终用分类器判断句子所属的隐式方面类别。与基线BERT及其他深度神经网络模型对比,所提模型在增强的SemEval隐式方面数据集上取得了更高的F₁与AUC值,证明了模型的有效性。

Combining dependency syntactic parsing with interactive attention mechanism for implicit aspect extraction

Implicit aspect extraction is important for improving the accuracy of fine-grained sentiment analysis. However, existing implicit aspect extraction techniques do not have strong generalization ability when dealing with large-scale data. To address the problem, this paper proposed an implicit aspect extraction model combining dependency syntactic parsing and interactive attention mechanism. First, the model generated the initial representation of the text by the pre-trained language model BERT. Then, it passed the initial representation to the self-attention layer guided by the dependency syntactic parsing. Due to the interactive attention mechanism, the model further extracted the results of the above two processes. Finally it used a classifier to determine the implicit aspect of the sentence. Compared with baseline BERT and other deep neural network models, the proposed model has achieved higher F1 and AUC on the enhanced SemEval implicit aspect dataset, which proves the effectiveness of the model.