  Entity linking, the task of mapping textual mentions to known entities, has
recently been tackled using contextualized neural networks. We address the
question whether these results -- reported for large, high-quality datasets
such as Wikipedia -- transfer to practical business use cases, where labels are
scarce, text is low-quality, and terminology is highly domain-specific. Using
an entity linking model based on BERT, a popular transformer network in natural
language processing, we show that a neural approach outperforms and complements
hand-coded heuristics, with improvements of about 20% top-1 accuracy. Also, the
benefits of transfer learning on a large corpus are demonstrated, while
fine-tuning proves difficult. Finally, we compare different BERT-based
architectures and show that a simple sentence-wise encoding (Bi-Encoder) offers
a fast yet efficient search in practice.
