The development of annotated datasets over the 21st century has helped us truly realize the power of deep learning. Most of the datasets created for the named-entity-recognition (NER) task are not domain specific. Finance domain presents specific challenges to the NER task and a domain specific dataset would help push the boundaries of finance research. In our work, we develop the first high-quality NER dataset for the finance domain. To set the benchmark for the dataset, we develop and test a weak-supervision-based framework for the NER task. We extend the current weak-supervision framework to make it employable for span-level classification. Our weak-ner framework and the dataset are publicly available on GitHub and Hugging Face.

翻译：21世纪以来，标注数据集的发展真正帮助我们实现了深度学习的强大能力。目前大多数为命名实体识别任务创建的数据集并非领域特定的。金融领域为命名实体识别任务带来了特定挑战，而一个领域特定的数据集将有助于推动金融研究的边界。在我们的工作中，我们开发了首个面向金融领域的高质量命名实体识别数据集。为建立该数据集的基准，我们构建并测试了一个基于弱监督的命名实体识别框架。我们将现有弱监督框架进行扩展，使其适用于跨度级分类。我们的弱监督命名实体识别框架及数据集已在GitHub和Hugging Face上公开发布。