Company2Vec -- German Company Embeddings based on Corporate Websites
This provides a more detailed company representation for banking and analytics, but it is incremental as it applies existing NLP methods to a new domain.
The paper tackles the problem of representing companies for business analytics by developing Company2Vec, which creates embeddings from unstructured website data using Word2Vec and dimensionality reduction, enabling fine-grained industry comparisons and applications like peer-firm identification.
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.