巴西葡萄牙语金融新闻多类分类大规模数据集
原标题：A Large-Scale Dataset for Multi-Class Financial News Classification in Brazilian Portuguese
AI 摘要
研究者发布了一个约16万篇巴西葡萄牙语金融新闻的大规模数据集，以缓解该领域数据稀缺问题。他们构建了六个类别（钢铁、石油、银行、金融科技、一般市场影响、无关），并分别用人工和LLM零样本标注子集。实验比较了余弦相似度、KNN和FAISS三种基于多语言语义嵌入的分类方法，发现任务对标签质量和类别分布高度敏感：人工标注子集上KNN最佳（准确率47.3%，F1 31.9%），而LLM标注子集准确率约50%但F1降至约20%。
正文节选
A Large-Scale Dataset for Multi-Class Financial News Classification in Brazilian Portuguese Abstract The development of Financial Natural Language Processing (NLP) systems for Brazilian Portuguese is hindered by the scarcity of domain-specific datasets. To address this, we present a large-scale corpus of approximately 160,000 Brazilian Portuguese financial news articles collected from web sources. To establish initial benchmarks for a weakly supervised multi-class classification approach based o