面向语法加权图文本的方面情感三元组抽取

Aspect sentiment triple extraction for grammar-weighted graph text

方面情感三元组抽取包括方面抽取、意见抽取和方面情感分类3项任务,以管道方式解决该任务的研究方法无法利用元素之间的交互信息,同时也会造成错误传播和冗余训练。基于此,提出一种基于门控注意力和加权图文本的方面情感三元组抽取方法。采用双向长短时记忆网络学习句子的序列特征表示;利用门控注意力单元学习单词之间的线性联系;利用语法距离加权图卷积网络增强三元组元素之间的交互;利用网格标记推理策略预测三元组。在4个公开数据集上进行实验,结果表明:所提方法可以有效增强三元组元素之间的交互,提高三元组抽取的准确率;同时,所提方法的F1值分别为57.94%、70.54%、61.95%和67.66%,与基准模型相比均有所提高。

Aspect sentiment triple extraction includes three tasks:aspect term extraction,opinion term extraction,and aspect sentiment classification.However,research methods that solve this task in a pipeline way cannot utilize the interaction information between elements,and will also cause error propagation and redundant training.To solve the above problems,an aspect sentiment triple extraction method based on gated attention and weighted graph text is proposed,which makes full use of the semantic and grammatical relationships between triple elements to enhance element interaction.Firstly,the model uses a bidirectional long-short-term memory network to learn the sequence feature representation of sentences.Secondly,a gated attention unit is used to learn linear connections between words.Thirdly,a grammatical distance-weighted graph convolutional network is employed to enhance the interactions between triplet elements.Finally,a grid tagging inference strategy is applied to predict triples.Experimental results on four public datasets show that the proposed method can effectively enhance the interaction between triple elements and improve the accuracy of triple extraction.Moreover,the F1 values of the proposed method are 57.94%,70.54%,61.95%and 67.66%,respectively,which are all improved compared to the baseline model.