2026年语义评价任务3中的经典方法：结合变压器模型和大语言模型生成的注释进行维度方面的情感分析
The Classics at SemEval-2026 Task 3: Combining Transformer Models and LLM-Generated Annotations for Dimensional Aspect-Based Sentiment Analysis
AI总结：
研究超越传统分类情感，预测情感“效价”和“唤醒”细粒度实值分数的方法。回归任务用基于变压器的编码器模型加权集成，俄语用大语言模型增强输入，提取任务微调解码器大语言模型进行结构化预测。
摘要：
本文提出了一种针对2026年语义评价任务3（维度方面的情感分析）的方法。我们研究了超越传统分类情感（如积极或消极）的方法，以预测情感“效价”（积极性）和“唤醒”（强度）的细粒度实值分数。我们参与两个子任务：为给定方面预测这些分数（子任务1）和提取完整的情感细节集，包括方面、类别和意见及其分数（子任务3）。我们用于回归任务的方法涉及基于变压器的编码器模型的加权集成。对于俄语，我们通过使用大语言模型（LLM）生成合成情感描述来进一步增强输入。对于提取任务，我们微调一个解码器LLM以执行结构化预测，使系统能够同时识别情感元素并估计其数值分数。
英文摘要：
This paper presents an approach to the SemEval-2026 Task 3: Dimensional Aspect-Based Sentiment Analysis. We investigate methods for moving beyond traditional categorical sentiment (e.g., positive or negative) to predict fine-grained, real-valued scores for sentiment "valence" (positivity) and "arousal" (intensity). We participate in two subtasks: predicting these scores for given aspects (Subtask 1) and extracting full sets of sentiment details, including aspects, categories, and opinions alongside their scores (Subtask 3). Our approach for the regression task involves a weighted ensemble of transformer-based encoder models. For the Russian language, we further enhance the input by using a large language model (LLM) to generate synthetic sentiment descriptions. For the extraction task, we fine-tune a decoder LLM to perform structured prediction, allowing the system to identify sentiment elements and estimate their numerical scores simultaneously.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
