Aspect-oriented sentiment analysis is a meticulous sentiment analysis task that aims to analyse the sentiment polarity of specific aspects. Most of the current research builds graph convolutional networks based on dep...Aspect-oriented sentiment analysis is a meticulous sentiment analysis task that aims to analyse the sentiment polarity of specific aspects. Most of the current research builds graph convolutional networks based on dependent syntactic trees, which improves the classification performance of the models to some extent. However, the technical limitations of dependent syntactic trees can introduce considerable noise into the model. Meanwhile, it is difficult for a single graph convolutional network to aggregate both semantic and syntactic structural information of nodes, which affects the final sentence classification. To cope with the above problems, this paper proposes a bi-channel graph convolutional network model. The model introduces a phrase structure tree and transforms it into a hierarchical phrase matrix. The adjacency matrix of the dependent syntactic tree and the hierarchical phrase matrix are combined as the initial matrix of the graph convolutional network to enhance the syntactic information. The semantic information feature representations of the sentences are obtained by the graph convolutional network with a multi-head attention mechanism and fused to achieve complementary learning of dual-channel features. Experimental results show that the model performs well and improves the accuracy of sentiment classification on three public benchmark datasets, namely Rest14, Lap14 and Twitter.展开更多
针对大规模开放在线课程(MOOC)质量评估维度单一、缺乏细粒度分析的问题,构建了一个以方面级情感分析为核心的语义切分至多维评估框架。基于中国大学MOOC平台中大数据与人工智能类课程的评论数据,利用通用信息抽取(universal informatio...针对大规模开放在线课程(MOOC)质量评估维度单一、缺乏细粒度分析的问题,构建了一个以方面级情感分析为核心的语义切分至多维评估框架。基于中国大学MOOC平台中大数据与人工智能类课程的评论数据,利用通用信息抽取(universal information extraction,UIE)工具抽取影响要素词,通过K-means聚类与变异系数法,构建了涵盖5个一级指标和10个二级指标的加权评价体系。设计了基于影响要素词定位的语义切分(impact element word-based targeted comment segmentation and classification,ITCSC)算法,将长评论切分为方面级短句,结合SKEP情感模型实现多维度量化分析,揭示了内容配置、服务评价等维度的表现特征。实验表明,该框架兼顾整体趋势与细节特征,为课程优化及选课提供数据支持,丰富了细粒度教育质量评估路径。展开更多
提出基于短语参数学习的主题模型TMPP(Topic Model based on Phrase Parameter)对在线评论中被评价实体的aspect和与之对应的rating进行抽取.TMPP具有三个特点:1)评论用"短语袋"表示;2)将标准的LDA中表示文档-主题的参数扩展...提出基于短语参数学习的主题模型TMPP(Topic Model based on Phrase Parameter)对在线评论中被评价实体的aspect和与之对应的rating进行抽取.TMPP具有三个特点:1)评论用"短语袋"表示;2)将标准的LDA中表示文档-主题的参数扩展为(aspect,rating)集;3)融合了先验知识.介绍了TMPP模型参数的物理含义、模型的生成过程以及先验知识的获取和表示方法;阐述了在TMPP模型中引入方面集聚类使用先验知识的原因与好处、TMPP模型提取(方面,等级)对形成(aspect,rating)摘要的原理.以真实的在线产品评论数据集为实验对象,在实验过程中引入先验知识的方面识别分析和等级预测精度分析,列出了五类产品相关方面和对立的情感词的实验结果.通过与已有的基线方法比较,实验表明若评论集中每篇评论有一个总体等级,TMPP能产生高质量的(aspect,rating)摘要.展开更多
文摘Aspect-oriented sentiment analysis is a meticulous sentiment analysis task that aims to analyse the sentiment polarity of specific aspects. Most of the current research builds graph convolutional networks based on dependent syntactic trees, which improves the classification performance of the models to some extent. However, the technical limitations of dependent syntactic trees can introduce considerable noise into the model. Meanwhile, it is difficult for a single graph convolutional network to aggregate both semantic and syntactic structural information of nodes, which affects the final sentence classification. To cope with the above problems, this paper proposes a bi-channel graph convolutional network model. The model introduces a phrase structure tree and transforms it into a hierarchical phrase matrix. The adjacency matrix of the dependent syntactic tree and the hierarchical phrase matrix are combined as the initial matrix of the graph convolutional network to enhance the syntactic information. The semantic information feature representations of the sentences are obtained by the graph convolutional network with a multi-head attention mechanism and fused to achieve complementary learning of dual-channel features. Experimental results show that the model performs well and improves the accuracy of sentiment classification on three public benchmark datasets, namely Rest14, Lap14 and Twitter.
文摘针对遥感图像中大纵横比目标因正样本不足而出现的学习不充分问题,提出一种基于形状自适应标签分配的遥感有向目标检测网络(shape-adaptive label assignment for oriented object detection network,SALANet)。首先,引入纵横比敏感系数建立目标几何特征与正样本数量的动态映射关系,缓解传统方法中固定分配规则引发的样本分布不平衡问题;其次,设计自适应标签分配策略,通过对交并比(intersection over union,IoU)进行排名实现高质量正样本选择;最后,提出中心轴先验,将圆形中心先验区扩展为目标中心轴的矩形区域,增强大纵横比目标的几何特征表征能力。在DOTAv1.0和HRSC2016数据集上的对比实验表明,SALANet分别取得0.777 1和0.932 3的平均精度均值(mean average precision,mAP),较基线方法RoI Transformer分别提升8.15%和2.87%。
文摘针对大规模开放在线课程(MOOC)质量评估维度单一、缺乏细粒度分析的问题,构建了一个以方面级情感分析为核心的语义切分至多维评估框架。基于中国大学MOOC平台中大数据与人工智能类课程的评论数据,利用通用信息抽取(universal information extraction,UIE)工具抽取影响要素词,通过K-means聚类与变异系数法,构建了涵盖5个一级指标和10个二级指标的加权评价体系。设计了基于影响要素词定位的语义切分(impact element word-based targeted comment segmentation and classification,ITCSC)算法,将长评论切分为方面级短句,结合SKEP情感模型实现多维度量化分析,揭示了内容配置、服务评价等维度的表现特征。实验表明,该框架兼顾整体趋势与细节特征,为课程优化及选课提供数据支持,丰富了细粒度教育质量评估路径。
文摘提出基于短语参数学习的主题模型TMPP(Topic Model based on Phrase Parameter)对在线评论中被评价实体的aspect和与之对应的rating进行抽取.TMPP具有三个特点:1)评论用"短语袋"表示;2)将标准的LDA中表示文档-主题的参数扩展为(aspect,rating)集;3)融合了先验知识.介绍了TMPP模型参数的物理含义、模型的生成过程以及先验知识的获取和表示方法;阐述了在TMPP模型中引入方面集聚类使用先验知识的原因与好处、TMPP模型提取(方面,等级)对形成(aspect,rating)摘要的原理.以真实的在线产品评论数据集为实验对象,在实验过程中引入先验知识的方面识别分析和等级预测精度分析,列出了五类产品相关方面和对立的情感词的实验结果.通过与已有的基线方法比较,实验表明若评论集中每篇评论有一个总体等级,TMPP能产生高质量的(aspect,rating)摘要.
