Company classification using zero-shot learning

TL;DR

This paper presents a zero-shot learning approach using pre-trained transformer models for classifying companies based on textual descriptions, reducing the need for labeled training data and streamlining classification processes.

Contribution

It introduces a novel application of zero-shot learning with NLP transformers for company classification, eliminating the need for category-specific training data.

Findings

Effective classification of companies without category-specific training data

Reduces time and resources compared to traditional classification methods

Potential for automating company classification processes

Abstract

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.