Company classification using zero-shot learning
- URL: http://arxiv.org/abs/2305.01028v2
- Date: Thu, 26 Oct 2023 20:19:37 GMT
- Title: Company classification using zero-shot learning
- Authors: Maryan Rizinski, Andrej Jankov, Vignesh Sankaradas, Eugene Pinsky,
Igor Miskovski, Dimitar Trajanov
- Abstract summary: We propose an approach for company classification using NLP and zero-shot learning.
We evaluate our approach on a dataset obtained through the Wharton Research Data Services (WRDS)
- Score: 0.0
- License: http://creativecommons.org/licenses/by/4.0/
- 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.
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