【摘要】 方面情感三元组抽取旨在从句子中识别方面词，意见词以及相应的情感极性。针对现有研究未充分考虑方面词和意见词之间的关联关系，以及存在语义信息提取不充分和背景知识利用不全面的问题，提出一种基于双编码器和知识增强的方面情感三元组抽取模型。首先，同时使用BERT和Bi-LSTM双编码器从不同层面挖掘句子中的语义信息，并融合外部情感知识增强文本的情感表达；其次，通过位置嵌入的交互注意力对方面和意见之间的关系进行交互迭代学习；最后，利用边界驱动表格填充的方法预测三元组。实验结果表明，该模型与主流模型GTS-BERT相比，在4个公开数据集上的F₁值分别提高了9.43个百分点、7.32个百分点、7.43个百分点和4.78个百分点，能够准确地进行三元组的提取。更多还原

【Abstract】 Aspect sentiment triplet extraction aims to identify aspect words, opinion words, and corresponding sentiment polarity from sentences. Existing studies have not fully considered the correlation relationship between aspect words and opinion words, and also suffer from the problems of insufficient semantic information extraction and incomplete utilization of background knowledge. To address the above issues, a dual encoder and knowledge enhancement aspect sentiment triplet extraction model is proposed. Firstly, dual encoders of BERT and Bi-LSTM are used simultaneously to mine semantic information in sentences from different levels and integrate external affective knowledge to enhance the sentiment expression of the text. Secondly, the relationship between aspects and opinions is learned interactively and iteratively by position embedding interactive attention. Finally, the triplet is predicted using a boundary-driven table-filling method. The experimental results show that the model can accurately perform triplet extraction by improving the F₁ values on the four public datasets by 9.43 percentage points, 7.32 percentage points, 7.43 percentage points and 4.78 percentage points, respectively, compared with the mainstream model GTS-BERT.更多还原