# Named entity recognition using GPT for identifying comparable companies

arXiv: https://arxiv.org/abs/2307.07420 · HTML: https://mlsanity.com/paper/2307.07420/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 7 of its 48 references that are in the map.
- First posted on arXiv 11 Jul 2023.

## Builds on (most cited first, 7 of 7)

- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [Language Models are Few-Shot Learners](https://mlsanity.com/paper/2005.14165/) (2020)
- [Finetuned Language Models Are Zero-Shot Learners](https://mlsanity.com/paper/2109.01652/) (2021)
- [How transferable are features in deep neural networks?](https://mlsanity.com/paper/1411.1792/) (2014)
- [Evaluation: from precision, recall and F-measure to ROC, informedness, markedness and correlation](https://mlsanity.com/paper/2010.16061/) (2020)
- [A Corpus Study and Annotation Schema for Named Entity Recognition and Relation Extraction of Business Products](https://mlsanity.com/paper/2004.03287/) (2020)
- [Transfer Learning in Spatial-Temporal Forecasting of the Solar Magnetic Field](https://mlsanity.com/paper/1911.03193/) (2019)

## Built on by (most cited first, 2 of 2)

- [A Survey of Large Language Models for Financial Applications: Progress, Prospects and Challenges](https://mlsanity.com/paper/2406.11903/) (2024)
- [Curating art exhibitions using machine learning](https://mlsanity.com/paper/2506.19813/) (2025)

## Abstract (arXiv)

For both public and private firms, comparable companies' analysis is widely used as a method for company valuation. In particular, the method is of great value for valuation of private equity companies. The several approaches to the comparable companies' method usually rely on a qualitative approach to identifying similar peer companies, which tend to use established industry classification schemes and/or analyst intuition and knowledge. However, more quantitative methods have started being used in the literature and in the private equity industry, in particular, machine learning clustering, and natural language processing (NLP). For NLP methods, the process consists of extracting product entities from e.g., the company's website or company descriptions from some financial database system and then to perform similarity analysis. Here, using companies' descriptions/summaries from publicly available companies' Wikipedia websites, we show that using large language models (LLMs), such as GPT from OpenAI, has a much higher precision and success rate than using the standard named entity recognition (NER) methods which use manual annotation. We demonstrate quantitatively a higher precision rate, and show that, qualitatively, it can be used to create appropriate comparable companies peer groups which could then be used for equity valuation.