摘要
基于方面的情感分类方法判断句子中给定实体或属性的情感极性。针对使用全局注意力机制计算属性词和句子其他词的注意力分数时,会导致模型关注到与属性词不相关的词,并且对于长距离的依赖词、否定词关注不足,不能检测到并列关系和短语的问题,提出了基于自注意力机制的语义加强模型(SRSAM)。该模型首先使用双向长短时记忆神经网络模型(bidirectional long short-term memory,BiLSTM)获取文本编码,其次用自注意力机制计算文本编码的多个语义编码,最后将属性词和语义编码交互后判断属性词在句中的情感极性。使用SemEval 2014数据集的实验表明,由于模型能发现长距离依赖和否定词,对并列关系和短语有一定检测效果,相比基础模型在分类精度上有0.6%~1.5%的提升。
Aspect-level sentiment classification determines the emotional polarity from sentence towards a specific aspect word.When using the global attention mechanism to calculate the attention scores of attribute words and other words of the sentence,the model will focus on words that are not related to the attribute words,and pay insufficient attention to long-distance dependent words and negative words,and cannot detect side-by-side relationships or phrases.To solve these problems,this paper proposed a semantic enhancement model based on the self-attention mechanism(SRSAM).The model first used the bidirectional long short-term memory model to obtain the text encoding,and then used the self-attention mechanism to calculate the multiple semantic encodings of the text encoding.Finally,it used the attribute words and semantic coding to interact to determine the emotional polarity of the attribute words in the sentence.The experiment on the SemEval 2014 dataset show that,since the model can find long-distance dependence and negative words,it has a certain detection effect on the parallel relationship and the phrase,and the classification accuracy is 0.6%~1.5%higher than the basic model.
作者
王拂林
刘丹
昌茜
Wang Fulin;Liu Dan;Chang Xi(Research Institute of Electronic Science&Technology,University of Electronic Science&Technology of China,Chengdu 611731,China)
出处
《计算机应用研究》
CSCD
北大核心
2020年第11期3227-3231,3245,共6页
Application Research of Computers
关键词
方面词
情感分类
自注意力机制
语义编码
aspect words
sentiment classification
self-attention mechanism
semantic coding
作者简介
王拂林(1994-),男,四川人,硕士研究生,主要研究方向为大数据与云计算(phelanwang@foxmail.com);刘丹(1969-),男,四川人,副教授,硕导,博士,主要研究方向为网络安全、大数据及云计算、分布式并行计算;昌茜(1994-),女,四川人,硕士研究生,主要研究方向为大数据与网络安全.
