基于注意力机制和BiLSTM-CRF的消极情绪意见目标抽取  

Negative-emotion Opinion Target Extraction Based on Attention and BiLSTM-CRF

作　　者：丁锋 孙晓[2] DING Feng;SUN Xiao(School of Computer Science and Information Engineering,Hefei University of Technology,Hefei 230601,China;Anhui Province Key Laboratory of Affective Computing and Advanced Intelligent Machine,Hefei University of Technology,Hefei 230601,China)

机构地区：[1]合肥工业大学计算机与信息学院,合肥230601 [2]合肥工业大学情感计算与先进智能机器安徽省重点实验室,合肥230601

出　　处：《计算机科学》2022年第2期223-230,共8页Computer Science

基　　金：国家自然科学基金(61976078)。

摘　　要：基于方面情感分析(Aspect-Based Sentiment Analysis,ABSA)是自然语言处理的热门课题,其中意见目标抽取和意见目标情感极性分类是ABSA的基本子任务之一。而很少有研究直接抽取特定情感极性的意见目标,尤其是抽取更有潜在价值的消极情绪意见目标。文中提出了一种全新的ABSA子任务--抽取消极情绪意见目标(Negative-Emotion Opinion Target Extraction,NE-OTE),并提出了基于注意力机制和单词与字符混合嵌入的BiLSTM-CRF模型(Attention-based BiLSTM-CRF with Word Embedding and Character Embedding,AB-CE),在双向长短时记忆网络(Bi-directional Long Short-Term Memory,BiLSTM)学习文本语义信息和捕获长距离双向语义依赖关系的基础上,通过注意力机制使模型更好地关注输入序列中的关键部分和捕获与意见目标及其情感倾向相关的隐含特征,最终通过CRF层预测句子级别的全局最佳标签序列,实现对消极情绪意见目标的抽取。文中基于主流ABSA任务基准数据集构建了3个NE-OTE任务数据集,并在这些数据集上进行了广泛的实验,实验结果显示,所提模型能够有效识别消极情绪意见目标,且识别效果明显优于其他基线模型,验证了所提方法的有效性。Aspect-based sentiment analysis(ABSA) is a popular topic for natural language processing,in which opinion target extraction and sentiment polarity classification of opinion target are one of the basic subtasks of ABSA.However,few studies directly extract the opinion targets of specific emotional polarity,especially the negative emotion opinion targets with more potential value.A new ABSA subtask--negative emotion opinion target extraction(NE-OTE) is proposed,and a BiLSTM-CRF model based on attention mechanism and character and word mixture embedding(AB-CE) is proposed.On the basis of bi-directional long short-term memory(BiLSTM) learning textual semantic information and capturing long distance bi-directional semantic dependency,through the attention mechanism,the model can better pay attention to the key parts in the input sequence and capture the implied characteristics related to the opinion target and its emotional tendency.Finally,the CRF layer can be used to predict the optimal tag sequence at the sentence level,so as to extract the negative emotional opinion target.This paper builds three NE-OTE task datasets based on the mainstream ABSA task baseline datasets and conducts extensive experiments on these datasets.Experimental results show that the model proposed in this paper can effectively identify the target of negative emotional opinions,and is significantly better than other baseline models,which verifies the effectiveness of the method proposed in this paper.

关 键 词：情感分析 消极情绪意见目标 注意力机制 双向长短时记忆网络 条件随机场 

分 类 号：TP391.1[自动化与计算机技术—计算机应用技术] TP183[自动化与计算机技术—计算机科学与技术]