  In the legal domain it is important to differentiate between words in
general, and afterwards to link the occurrences of the same entities. The topic
to solve these challenges is called Named-Entity Linking (NEL). Current
supervised neural networks designed for NEL use publicly available datasets for
training and testing. However, this paper focuses especially on the aspect of
applying transfer learning approach using networks trained for NEL to legal
documents. Experiments show consistent improvement in the legal datasets that
were created from the European Union law in the scope of this research. Using
transfer learning approach, we reached F1-score of 98.90\% and 98.01\% on the
legal small and large test dataset.
