# Company2Vec -- German Company Embeddings based on Corporate Websites

arXiv: https://arxiv.org/abs/2307.09332 · HTML: https://mlsanity.com/paper/2307.09332/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 7 of its 31 references that are in the map.
- First posted on arXiv 18 Jul 2023.

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

- [Efficient Estimation of Word Representations in Vector Space](https://mlsanity.com/paper/1301.3781/) (2013)
- [Distributed Representations of Words and Phrases and their Compositionality](https://mlsanity.com/paper/1310.4546/) (2013)
- [Distributed Representations of Sentences and Documents](https://mlsanity.com/paper/1405.4053/) (2014)
- [SimLex-999: Evaluating Semantic Models with (Genuine) Similarity Estimation](https://mlsanity.com/paper/1408.3456/) (2014)
- [Top2Vec: Distributed Representations of Topics](https://mlsanity.com/paper/2008.09470/) (2020)
- [Wikipedia2Vec: An Efficient Toolkit for Learning and Visualizing the Embeddings of Words and Entities from Wikipedia](https://mlsanity.com/paper/1812.06280/) (2018)
- [Company classification using machine learning](https://mlsanity.com/paper/2004.01496/) (2020)

## Built on by (most cited first, 3 of 3)

- [Multimodal Document Analytics for Banking Process Automation](https://mlsanity.com/paper/2307.11845/) (2023)
- [Unlocking NACE Classification Embeddings with OpenAI for Enhanced Analysis and Processing](https://mlsanity.com/paper/2409.11524/) (2024)
- [A Computational Framework for Modelling Organisation-Level Semantic Identity from Longitudinal Textual Data](https://mlsanity.com/paper/2609.29584/) (2026)

## Abstract (arXiv)

With Company2Vec, the paper proposes a novel application in representation learning. The model analyzes business activities from unstructured company website data using Word2Vec and dimensionality reduction. Company2Vec maintains semantic language structures and thus creates efficient company embeddings in fine-granular industries. These semantic embeddings can be used for various applications in banking. Direct relations between companies and words allow semantic business analytics (e.g. top-n words for a company). Furthermore, industry prediction is presented as a supervised learning application and evaluation method. The vectorized structure of the embeddings allows measuring companies similarities with the cosine distance. Company2Vec hence offers a more fine-grained comparison of companies than the standard industry labels (NACE). This property is relevant for unsupervised learning tasks, such as clustering. An alternative industry segmentation is shown with k-means clustering on the company embeddings. Finally, this paper proposes three algorithms for (1) firm-centric, (2) industry-centric and (3) portfolio-centric peer-firm identification.