Named-Entity Linking Using Deep Learning For Legal Documents: A Transfer Learning Approach
This work addresses entity linking challenges for legal professionals by improving accuracy on domain-specific documents, though it is incremental as it adapts existing methods to a new domain.
The paper tackled the problem of Named-Entity Linking (NEL) in legal documents by applying a transfer learning approach, achieving F1-scores of 98.90% and 98.01% on small and large legal test datasets derived from European Union law.
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.