Deep learning for named-entity linking with transfer learning for legal documents

Ahmed Elnaggar, Robin Otto, Florian Matthes

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

11 Scopus citations

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.

Original languageEnglish
Title of host publicationAICCC 2018 - Proceedings of 2018 Artificial Intelligence and Cloud Computing Conference
PublisherAssociation for Computing Machinery
Pages23-28
Number of pages6
ISBN (Electronic)9781450366236
DOIs
StatePublished - 21 Dec 2018
Event2018 International Conference on Artificial Intelligence and Cloud Computing, AICCC 2018 - Tokyo, Japan
Duration: 21 Dec 201823 Dec 2018

Publication series

NameACM International Conference Proceeding Series

Conference

Conference2018 International Conference on Artificial Intelligence and Cloud Computing, AICCC 2018
Country/TerritoryJapan
CityTokyo
Period21/12/1823/12/18

Keywords

  • Deep Learning
  • Legal Domain
  • Named-entity Linking
  • Transfer Learning

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