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Multilingual Fine-Grained Entity Typing

  • Marieke van Erp
  • Piek Vossen
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10318)

Abstract

Many entity recognition approaches classify recognised entities into a limited set of coarse-grained entity types. However, for deeper natural language analysis and end-user tasks, fine-grained entity types are more useful. For example, while standard named entity recognition may determine that an entity is a person knowing whether that entity is a politician or an actor is important for determining whether, in a subsequent relation extraction task, a relation should be acts or governs. Currently, fine-grained entity typing has only been investigated for English. In this paper, we present a fine-grained entity typing system for Dutch and Spanish using training data extracted from Wikipedia and DBpedia. Our system achieves comparable performance to English with an F\(_{1}\) measure of .90 on over 40 types for both Dutch and Spanish.

Notes

Acknowledgements

The research for this paper was made possible by the CLARIAH-CORE project financed by NWO.

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Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  1. 1.Computational Lexicology and Terminology Lab, The Network InstituteVrije Universiteit AmsterdamAmsterdamThe Netherlands

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