发表机构
Universidade da Coruña; Graz University of Technology; University of Copenhagen（拉科鲁尼亚大学; 格拉茨工业大学; 哥本哈根大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本研究将结构化情感分析转化为依存图解析任务，利用线性化图编码的序列标注方法，在五种语言七个数据集上取得与领先复杂单模型相当的性能。
AI 中文摘要
本研究解决结构化情感分析问题，其目标是获取细粒度情感图，其中节点代表情感持有者、目标和表达式的文本片段，弧定义它们之间的关系。我们提出的方法将该任务转化为依存图解析，但与传统解析方法不同，通过序列标注解决该任务。为此，我们利用线性化图编码的最新进展，允许为输入中的每个单词分配一个标签，有效捕获依存图的结构。我们在涵盖五种语言（英语、西班牙语、挪威语、巴斯克语和加泰罗尼亚语）的七个数据集上进行实验，结果显示性能可与领先的、更复杂的单模型方法相媲美。
英文摘要
This study addresses the problem of structured sentiment analysis, whose goal is to obtain a fine-grained sentiment graph where the nodes represent spans of sentiment holders, targets, and expressions, while the arcs define the relationships among them. Our proposed approach casts the task as dependency graph parsing, but departs from traditional parsing methods by solving it through sequence labeling. To do so, we leverage recent advances in linearized graph encodings that allow each word in the input to be assigned a label, effectively capturing the structure of the dependency graph. We conducted experiments on seven datasets spanning five languages (English, Spanish, Norwegian, Basque, and Catalan), showing performance competitive with leading, more complex single-model approaches.
