arXiv cs.CL· Leomax Bandeira, Gabriel Aguiar, Dalton C\'ezane Gomes Valadares, Luiz Pereira, Helder Fernando de Ara\'ujo Oliveira, Gustavo Xavier, Andson M. Balieiro·· 2 天前AI 评分15
巴西葡萄牙语金融新闻多类分类大规模数据集发布
A Large-Scale Dataset for Multi-Class Financial News Classification in Brazilian Portuguese
AI 导读
研究者构建了约 16 万篇巴西葡萄牙语金融新闻语料库，并基于多语言语义嵌入对比了余弦相似度、KNN 和 FAISS 三种相似度方法。在人工标注子集上 KNN 表现最佳，准确率 47.3%、F1 31.9%；在 LLM 标注子集上准确率约 50% 但 F1 降至近 20%，显示标签不平衡与弱标注噪声的负面影响。
正文 · 原文
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 on semantic similarity, we annotated subsets of this corpus both manually and using a Large Language Model (LLM). We evaluated three similarity-based methods -- cosine similarity, K-Nearest Neighbors (KNN), and FAISS-based nearest-neighbor search -- using multilingual semantic embeddings. Our experiments reveal that the task is highly sensitive to label quality and class distribution. On the manually labeled subset, KNN achieved the best performance (47.3% accuracy, 31.9% F1-score), closely followed by FAISS. Conversely, on the LLM-annotated subset, while accuracy reached approximately 50%, F1-scores dropped to near 20%, highlighting the negative impact of label imbalance and noisy weak annotations. These findings establish foundational baselines for a challenging task, underscoring the critical need for high-quality manual annotations. Ultimately, this dataset and our preliminary results provide a reference point for future research in Brazilian Portuguese Financial NLP.
| Comments: | Paper accepted for presentation at SMC 2026 |
| Subjects: | Computation and Language (cs.CL); Machine Learning (cs.LG) |
| Cite as: | arXiv:2610.20630 [cs.CL] |
| (or arXiv:2610.20630v1 [cs.CL] for this version) | |
| https://doi.org/10.48550/arXiv.2610.20630 arXiv-issued DOI via DataCite |
Submission history
From: Dalton Valadares [view email]
[v1]
Tue, 29 Sep 2026 19:06:20 UTC (228 KB)
来源：arXiv cs.CL · arxiv.org