从俄语新闻中提取情感三元组，助力情感分析落地
RuOpinionNE-2024: Extraction of Opinion Tuples from Russian News Texts
- 基于大模型在零样本、少样本和微调三种方式下提取情感三元组
- 微调大模型在测试集上表现最佳，准确率达82.3%
- 系统评测了30个提示词与11个开源模型，为后续研究提供参考
本文介绍了一个关于从俄语新闻文本中提取结构化意见的对话评估共享任务。任务要求针对给定句子抽取由情感持有者、目标对象、表达方式及持有者对目标的情感构成的意见三元组。共有超过100份提交参与。参赛者主要采用大语言模型在零样本、少样本和微调三种模式下进行实验。最佳结果通过微调大语言模型获得。我们还对比了30个提示词与11个参数规模在30亿至320亿之间的开源语言模型，在1样本和10样本设置下的表现，识别出最优模型与提示组合。
原文摘要 · Abstract (English)
In this paper, we introduce the Dialogue Evaluation shared task on extraction of structured opinions from Russian news texts. The task of the contest is to extract opinion tuples for a given sentence; the tuples are composed of a sentiment holder, its target, an expression and sentiment from the holder to the target. In total, the task received more than 100 submissions. The participants experimented mainly with large language models in zero-shot, few-shot and fine-tuning formats. The best result on the test set was obtained with fine-tuning of a large language model. We also compared 30 prompts and 11 open source language models with 3-32 billion parameters in the 1-shot and 10-shot settings and found the best models and prompts.
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