In recent years, natural language processing (NLP) has become increasingly important in a variety of business applications, including sentiment analysis, text classification, and named entity recognition. In this paper, we propose an approach for company classification using NLP and zero-shot learning. Our method utilizes pre-trained transformer models to extract features from company descriptions, and then applies zero-shot learning to classify companies into relevant categories without the need for specific training data for each category. We evaluate our approach on publicly available datasets of textual descriptions of companies, and demonstrate that it can streamline the process of company classification, thereby reducing the time and resources required in traditional approaches such as the Global Industry Classification Standard (GICS). The results show that this method has potential for automation of company classification, making it a promising avenue for future research in this area.

翻译：近年来，自然语言处理（NLP）在情感分析、文本分类和命名实体识别等多种商业应用中日益重要。本文提出了一种结合NLP与零样本学习（zero-shot learning）的公司分类方法。该方法利用预训练Transformer模型从公司描述中提取特征，进而通过零样本学习将公司归入相关类别，无需为每个类别准备特定的训练数据。我们在公开的公司文本描述数据集上对方法进行了评估，结果表明该方法能够简化公司分类流程，从而减少传统方法（如全球行业分类标准GICS）所需的时间与资源。实验结果证实，该方法具有自动化公司分类的潜力，为这一领域的未来研究提供了有前景的方向。