语义和句法依赖增强的跨度级方面情感三元组抽取

本研究针对目前跨度级别的方面情感三元组抽取模型忽视词性和句法知识的问题且存在三元组冲突的情况, 提出了语义和句法依赖增强的跨度级方面情感三元组抽取模型SSES-SPAN (semantic and syntactic enhanced span-based aspect sentiment triplet extraction). 首先, 在特征编码器中引入词性知识和句法依赖知识, 使模型能够更精准地区分文本中的方面词和观点词, 并且更深入地理解它们之间的关系. 具体而言, 对于词性信息, 采用了一种加权求和的方法, 将词性上下文表示与句子上下文表示融合得到语义增强表示, 以帮助模型准确提取方面词和观点词. 对于句法依赖信息, 采用注意力机制引导的图卷积网络捕捉句法依赖特征得到句法依赖增强表示, 以处理方面词和观点词之间的复杂关系. 此外, 鉴于跨度级别的输入缺乏互斥性的保证, 采用推理策略以消除冲突三元组. 在基准数据集上进行的大量实验表明, 我们提出的模型在效果和鲁棒性方面超过了最先进的方法.

Semantic and Syntactic Dependency Enhanced Span-based Aspect Sentiment Triplet Extraction

This study aims to address the issues of current span-based aspect sentiment triplet extraction models, which ignore part-of-speech and syntactic knowledge and encounter conflicts in triplets. A semantic and syntactic enhanced span-based aspect sentiment triplet extraction model named SSES-SPAN is proposed. Firstly, part-of-speech and syntactic dependency knowledge is introduced into the feature encoder to enable the model to more accurately distinguish aspect and opinion terms in the text and gain a deeper understanding of their relationships. Specifically, for part-of-speech information, a weighted sum approach is employed to fuse part-of-speech contextual representation with sentence contextual representation to obtain semantic enhanced representation, aiding in the precise extraction of aspect and opinion terms. For syntactic dependency information, attention-guided graph convolution networks are used to capture syntactic dependency features and obtain syntactic dependency enhanced representation to handle complex relationships between aspect and opinion terms. Furthermore, considering the lack of a mutual exclusivity guarantee in span-level inputs, an inference strategy is employed to eliminate conflicting triplets. Extensive experiments on benchmark datasets demonstrate that the proposed model outperforms state-of-the-art methods in terms of effectiveness and robustness.