Company classification using zero-shot learning
Date Issued
2023-07
Author(s)
Rizinski, Maryan
Jankov, Andrej
Sankaradas, Vignesh
Pinsky, Eugene
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 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.
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.
Subjects
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