基于句法知识和常识知识适配器的面向方面级情感分类网络

Guojun Lu, Haibo Yu, Yun Xue, Zhixun Qiu, Weiyu Zhong
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引用次数: 1

摘要

方面级情感分类是一种最明显的方法,它被定义为一种从大量文本中提取重要信息的自动化技术。然而,目前的研究在ALSC任务中仍然存在局限性(如依赖关系解析的准确性和对常识性知识的忽视)。本文提出了一种基于句法知识和常识知识适配器的网络,分别处理位置信息、句法结构和外部知识。我们的模型的性能在三个基准数据集上进行了评估。实验结果表明,与现有的方法相比,我们的模型是ALSC任务的最佳选择。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
SCAN:Syntactic Knowledge and Commonsense Knowledge Adapter Based Network for Aspect-level Sentiment Classification
Aspect-level sentiment classification is a most pronounced approach, which is defined as an automated technique to extract significant information from a large number of texts. However, current research still has limitations in ALSC tasks (e.g. accuracy of dependency parsing and overlook of commonsense knowledge). In this work, we propose a syntactic knowledge and commonsense knowledge adapter based network, which deals with the position information, syntactic structure and external knowledge, respectively. The performance of our model is evaluated on the three benchmark datasets. Experimental results demonstrate that our model is a best alternative in ALSC tasks compared with the state-of-the-art methods.
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