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Enhancing Entity Linking by Combining NER Models

  • Julien Plu
  • Giuseppe Rizzo
  • Raphaël TroncyEmail author
Conference paper
Part of the Communications in Computer and Information Science book series (CCIS, volume 641)

Abstract

Numerous entity linking systems are addressing the entity recognition problem by using off-the-shelf NER systems. It is, however, a difficult task to select which specific model to use for these systems, since it requires to judge the level of similarity between the datasets which have been used to train models and the dataset at hand to be processed in which we aim to properly recognize entities. In this paper, we present the newest version of ADEL, our adaptive entity recognition and linking framework, where we experiment with an hybrid approach mixing a model combination method to improve the recognition level and to increase the efficiency of the linking step by applying a filter over the types. We obtain promising results when performing a 4-fold cross validation experiment on the OKE 2016 challenge training dataset. We also demonstrate that we achieve better results that in our previous participation on the OKE 2015 test set. We finally report the results of ADEL on the OKE 2016 test set and we present an error analysis highlighting the main difficulties of this challenge.

Keywords

Entity recognition Entity linking Entity filtering Model combination OKE challenge ADEL 

Notes

Acknowledgments

This work was partially supported by the innovation activity 3cixty (14523) of EIT Digital and by the European Union’s H2020 Framework Programme via the FREME Project (644771).

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Copyright information

© Springer International Publishing Switzerland 2016

Authors and Affiliations

  1. 1.EURECOMSophia AntipolisFrance
  2. 2.ISMBTurinItaly

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