结合语法结构和语义信息的情感三元组提取

Sentiment Triple Extraction Combining Grammatical Structure and Semantic Information

针对目前大多数方面情感三元组提取方法存在着没有充分考虑语法结构和语义相关性的问题.本文提出一种结合语法结构和语义信息的方面情感三元组提取模型,首先提出使用依赖解析器得到所有依赖弧的概率矩阵构建语法图,提取丰富的语法结构信息.其次利用自注意力机制构建语义图,表示单词与单词之间的语义相关性,从而减低噪声词的干扰.最后设计了一个相互仿射变换层,让模型可以更好地交换语法图和语义图之间的相关特征,提升模型情感三元组提取的表现.在多个公开数据集上进行验证.实验表明,与现有的情感三元组提取模型相比,精确度(P)、召回率(R)和F1值整体都有提高,验证了结合语法结构和语义信息在方面情感三元组提取的有效性.

Most of the current aspect sentiment triplet extraction methods do not fully consider syntactic structure and semantic relevance.This study proposes an aspect sentiment triplet extraction model that combines syntactic structure and semantic information.First,the study proposes to construct a grammatical graph with a dependency parser to get the probability matrices of all dependency arcs,extracting rich information of syntactic structure.Second,it utilizes the selfattention mechanism to construct a semantic graph,which represents the semantic correlation between words,thus reducing the interference of noisy words.Finally,a mutual affine transformation layer is designed to allow the model to better exchange the relevant features between the syntactic graph and semantic graph to improve the performance of the model in sentiment triplet extraction.The model is validated on several public datasets.The experiments show that compared with the existing sentiment triplet extraction models,the precision(P),recall(R),and F1 value are all improved.This validates the effectiveness of combining syntactic structure and semantic information in aspect sentiment triplet extraction.