Company classification using zero-shot learning
This work addresses the problem of automating company classification for business applications, but it appears incremental as it applies existing zero-shot learning techniques to a new domain.
The paper tackles company classification by using NLP and zero-shot learning to categorize companies from textual descriptions without category-specific training data, demonstrating potential for automation and reduced resource use compared to traditional methods like GICS.
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 a dataset obtained through the Wharton Research Data Services (WRDS), which comprises textual descriptions of publicly traded companies. We demonstrate that the approach 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.