Incorporating explicit syntactic dependency for aspect level sentiment classification
Incorporating explicit syntactic dependency for aspect level sentiment classification
复制标题
结合显式句法依赖来进行方面级别的情感分类
DOI：
10.1016/j.neucom.2021.05.078
复制
发表时间：
2021-06-15
期刊：
影响因子：
6
通讯作者：
Cheng, Xueqi
中科院分区：
文献类型：
--
作者：
Ke, Wenjun;Gao, Jinhua;Cheng, Xueqi
方面级情感分类旨在从句子中提取针对特定方面表达的细粒度情感。这项任务的关键在于连接方面及其各自的情感背景。现有的方法通过注意机制捕获的词之间的语义相似度或句法结构中的词之间的结构接近度来测量方面和上下文词之间的依赖权重。然而，在这两组方法未能充分利用明确的句法依赖，我们认为这应该是至关重要的，以确定情绪的背景。在本文中，我们提出了一种新的句法依赖为基础的注意力网络（SDATT），将明确的句法依赖方面的水平情感分类。SDATT首先对每个词和方面之间的依赖路径进行建模，以表征每个词的面向方面的句法表示。生成的句法表示稍后被馈送到注意力层，以帮助推断用于情感预测的依赖权重。在五个基准数据集上的实验结果表明，该模型的上级性能优于最先进的基线。(c)2021爱思唯尔有限公司版权所有。
Aspect level sentiment classification aims to extract fine-grained sentiment expressed towards specific aspects from a sentence. The key to this task lies in connecting aspects and their respective sentiment contexts. Existing methods measure the dependency weights between aspects and context words via either the semantic similarity between words captured by attention mechanism or the structural proximity between words in syntactic structures. However, methods in both groups fail to fully exploit explicit syntactic dependency, which we argue should be critical to identify sentiment contexts. In this paper, we propose a novel syntactic-dependency-based attention network (SDATT) to incorporate explicit syntactic dependency for aspect level sentiment classification. SDATT first models the dependency path between each word and the aspect to characterize aspect-oriented syntactic representation of each word. The generated syntactic representations are later fed into the attention layer to help infer the dependency weights for sentiment prediction. Experimental results on five benchmark datasets show the superior performance of the proposed model over state-of-the-art baselines. (c) 2021 Elsevier B.V. All rights reserved.