针对语法依赖树存在多个方面词相互干扰的依赖信息、无效单词,以及标点符号带来的冗余信息和方面词与对应情感词之间的关联性较弱等问题,提出一种融合强关联依赖和简洁语法的方面级情感分析模型(SADCS)。首先,构建情感词性(POS)列表,通...针对语法依赖树存在多个方面词相互干扰的依赖信息、无效单词,以及标点符号带来的冗余信息和方面词与对应情感词之间的关联性较弱等问题,提出一种融合强关联依赖和简洁语法的方面级情感分析模型(SADCS)。首先,构建情感词性(POS)列表,通过该列表加强方面词与对应情感的相关性;其次,构建融合POS和依赖关系的联合列表,通过该联合列表去除已优化的依赖树无效单词与标点符号的冗余信息;再次,将优化后的依赖树与图注意力网络(GAT)结合建模提取上下文特征;最后,与依赖关系类型的特征信息进行交互学习并融合特征,增强特征表示,最终使分类器能高效预测每个方面词的情感极性。将所提模型在4个公开数据集上进行实验分析,与DMF-GAT-BERT(Dynamic Multichannel Fusion mechanism based on the GAT and BERT(Bidirectional Encoder Representations from Transformers))模型相比,所提模型的准确率分别提高了1.48、1.81、0.09和0.44个百分点。实验结果表明,所提模型能够有效增强方面词与情感词的联系,使方面词情感极性的预测更准确。展开更多
The exponential Randić index has important applications in the fields of biology and chemistry. The exponential Randić index of a graph G is defined as the sum of the weights e 1 d( u )d( v ) of all edges uv of G, whe...The exponential Randić index has important applications in the fields of biology and chemistry. The exponential Randić index of a graph G is defined as the sum of the weights e 1 d( u )d( v ) of all edges uv of G, where d( u ) denotes the degree of a vertex u in G. The paper mainly provides the upper and lower bounds of the exponential Randić index in quasi-tree graphs, and characterizes the extremal graphs when the bounds are achieved.展开更多
【目的】为全面梳理树木干旱致死相关研究的进展和热点,以期为科研人员提供该领域当前进展的全面认识。【方法】基于中国知网(CNKI)和Web of Science(WOS)所收录的发表于2000-2023年期间的相关论文,利用CiteSpace分析筛选出的153篇中文...【目的】为全面梳理树木干旱致死相关研究的进展和热点,以期为科研人员提供该领域当前进展的全面认识。【方法】基于中国知网(CNKI)和Web of Science(WOS)所收录的发表于2000-2023年期间的相关论文,利用CiteSpace分析筛选出的153篇中文和1757篇英文文献的作者、机构、国家(地区)、关键词等信息。【结果】树木干旱死亡领域的研究成果丰富,超过50%的研究成果来自中国、西班牙和美国。国内外研究主要侧重于碳饥饿、水力失衡和水分胁迫等主题,也针对干旱、树木死亡、气候变化和极端气候等热点问题结合本土树种开展了大量研究。从相关机制来看,主要有水力失衡和碳饥饿两个研究方向;就研究对象而言,则主要集中于欧洲山毛榉和巨型红杉。【结论】通过对树木干旱致死文献的分析,旨在清晰地展示国内外干旱致死的研究现状和热点,为未来的进一步研究提供参考。展开更多
Aiming at the problem that existing models in aspect-level sentiment analysis cannot fully and effectively utilize sentence semantic and syntactic structure information, this paper proposes a graph neural network-base...Aiming at the problem that existing models in aspect-level sentiment analysis cannot fully and effectively utilize sentence semantic and syntactic structure information, this paper proposes a graph neural network-based aspect-level sentiment classification model. Self-attention, aspectual word multi-head attention and dependent syntactic relations are fused and the node representations are enhanced with graph convolutional networks to enable the model to fully learn the global semantic and syntactic structural information of sentences. Experimental results show that the model performs well on three public benchmark datasets Rest14, Lap14, and Twitter, improving the accuracy of sentiment classification.展开更多
文摘针对语法依赖树存在多个方面词相互干扰的依赖信息、无效单词,以及标点符号带来的冗余信息和方面词与对应情感词之间的关联性较弱等问题,提出一种融合强关联依赖和简洁语法的方面级情感分析模型(SADCS)。首先,构建情感词性(POS)列表,通过该列表加强方面词与对应情感的相关性;其次,构建融合POS和依赖关系的联合列表,通过该联合列表去除已优化的依赖树无效单词与标点符号的冗余信息;再次,将优化后的依赖树与图注意力网络(GAT)结合建模提取上下文特征;最后,与依赖关系类型的特征信息进行交互学习并融合特征,增强特征表示,最终使分类器能高效预测每个方面词的情感极性。将所提模型在4个公开数据集上进行实验分析,与DMF-GAT-BERT(Dynamic Multichannel Fusion mechanism based on the GAT and BERT(Bidirectional Encoder Representations from Transformers))模型相比,所提模型的准确率分别提高了1.48、1.81、0.09和0.44个百分点。实验结果表明,所提模型能够有效增强方面词与情感词的联系,使方面词情感极性的预测更准确。
文摘The exponential Randić index has important applications in the fields of biology and chemistry. The exponential Randić index of a graph G is defined as the sum of the weights e 1 d( u )d( v ) of all edges uv of G, where d( u ) denotes the degree of a vertex u in G. The paper mainly provides the upper and lower bounds of the exponential Randić index in quasi-tree graphs, and characterizes the extremal graphs when the bounds are achieved.
文摘【目的】为全面梳理树木干旱致死相关研究的进展和热点,以期为科研人员提供该领域当前进展的全面认识。【方法】基于中国知网(CNKI)和Web of Science(WOS)所收录的发表于2000-2023年期间的相关论文,利用CiteSpace分析筛选出的153篇中文和1757篇英文文献的作者、机构、国家(地区)、关键词等信息。【结果】树木干旱死亡领域的研究成果丰富,超过50%的研究成果来自中国、西班牙和美国。国内外研究主要侧重于碳饥饿、水力失衡和水分胁迫等主题,也针对干旱、树木死亡、气候变化和极端气候等热点问题结合本土树种开展了大量研究。从相关机制来看,主要有水力失衡和碳饥饿两个研究方向;就研究对象而言,则主要集中于欧洲山毛榉和巨型红杉。【结论】通过对树木干旱致死文献的分析,旨在清晰地展示国内外干旱致死的研究现状和热点,为未来的进一步研究提供参考。
文摘Aiming at the problem that existing models in aspect-level sentiment analysis cannot fully and effectively utilize sentence semantic and syntactic structure information, this paper proposes a graph neural network-based aspect-level sentiment classification model. Self-attention, aspectual word multi-head attention and dependent syntactic relations are fused and the node representations are enhanced with graph convolutional networks to enable the model to fully learn the global semantic and syntactic structural information of sentences. Experimental results show that the model performs well on three public benchmark datasets Rest14, Lap14, and Twitter, improving the accuracy of sentiment classification.
