Blog
About

4
views
0
recommends
+1 Recommend
0 collections
    0
    shares
      • Record: found
      • Abstract: found
      • Article: found
      Is Open Access

      Named-Entity Linking Using Deep Learning For Legal Documents: A Transfer Learning Approach

      Preprint

      , ,

      Read this article at

      Bookmark
          There is no author summary for this article yet. Authors can add summaries to their articles on ScienceOpen to make them more accessible to a non-specialist audience.

          Abstract

          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.

          Related collections

          Most cited references 2

          • Record: found
          • Abstract: not found
          • Conference Proceedings: not found

          Deep transfer metric learning

            Bookmark
            • Record: found
            • Abstract: not found
            • Conference Proceedings: not found

            A low-cost, high-coverage legal named entity recognizer, classifier and linker

              Bookmark

              Author and article information

              Journal
              15 October 2018
              Article
              1810.06673

              http://arxiv.org/licenses/nonexclusive-distrib/1.0/

              Custom metadata
              10 pages, 2 figures, 3 tables
              cs.LG cs.AI cs.CL stat.ML

              Theoretical computer science, Machine learning, Artificial intelligence

              Comments

              Comment on this article