English(EN) "You are an expert annotator": Automatic Best-Worst-Scaling Annotations for Emotion Intensity Modeling

使用大型语言模型的自动化情感强度标注达到接近人类的性能

作者 PulseAugur 编辑部 · [1 个来源] · 2026-09-17 04:00

研究人员开发了一种自动标注文本中情感强度的方法，解决了创建自然语言处理任务数据集的关键瓶颈。这种新方法利用大型语言模型进行比较标注，特别是采用了最佳-最差排序技术，该技术被证明比直接评分更可靠。在这些自动标注上微调的Transformer回归器，其性能几乎与在手动标注数据上训练的模型相当。 AI

影响为情感强度等连续标签自动标注可以加速自然语言处理的研究和模型开发。

排序理由该集群描述了一篇研究论文，详细介绍了一种用于情感强度建模的文本数据自动标注新方法。[lever_c_demoted from research: ic=1 ai=1.0]

AI 生成摘要 · Google Gemini · 来自 1 个来源。我们如何撰写摘要 →

本文如何被排名

Signal score: 11 / 100; Composite score across the factors below. Higher = stronger signal that this story matters right now.
Newsworthiness bucket: Tool; 该集群描述了一篇研究论文，详细介绍了一种用于情感强度建模的文本数据自动标注新方法。[lever_c_demoted from research: ic=1 ai=1.0]
Source corroboration: Single-source cluster; Only one publisher covered this so far. Single-source stories can still rank when the publisher is high-authority, but they lack cross-source corroboration.
Topics: paper, product; Editorial topic classification. Feeds into how the story surfaces on /topic/<slug> hub pages and into the per-entity coverage mix.
AI-industry relevance: High; Clearly on-topic for AI-industry coverage.
Story freshness: Same-day; Cluster formed today. Ranking reflects the current source set at time of score.

完整方法见我们的编辑标准。

arXiv cs.CL TIER_1 English(EN) · Christopher Bagdon, Prathamesh Karmalker, Harsha Gurulingappa, Roman Klinger · 2026-09-17 04:00

"你是专家标注员"：用于情感强度建模的自动最佳-最差-缩放标注

arXiv:2403.17612v3 Announce Type: replace Abstract: Labeling corpora constitutes a bottleneck to create models for new tasks or domains. Large language models mitigate the issue with automatic corpus labeling methods, particularly for categorical annotations. Some NLP tasks such …