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International Conference on Language, Data and Knowledge

LDK 2017: Language, Data, and Knowledge pp 262–275Cite as

Multilingual Fine-Grained Entity Typing

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Part of the book series: Lecture Notes in Computer Science ((LNAI,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.

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Notes

  1. 1.

    https://www.mpi-inf.mpg.de/departments/databases-and-information-systems/research/yago-naga/yago/.

  2. 2.

    https://wordnet.princeton.edu/.

  3. 3.

    https://dumps.wikimedia.org/backup-index.html.

  4. 4.

    https://github.com/attardi/wikiextractor.

  5. 5.

    Using the wikilinks and instance types dumps from the latest DBpedia, version 2016-04 http://wiki.dbpedia.org/downloads-2016-04.

  6. 6.

    The types we could not map were the following: location/structure/government, organization/stock_exchange, other/health, other/living_thing, other/product/car, other/product/computer, person/education, person/education/student, person/education/teacher.

  7. 7.

    Although there is more text in the Spanish DBpedia, we only included a sample here to showcase the adaptability of the approach to other languages.

  8. 8.

    https://github.com/facebookresearch/fastText.

  9. 9.

    If an entity X has types location/structure and organisation/education assigned to it, two instances are generated namely X, location/structure and X, organisation/education.

  10. 10.

    The number of types from levels 1–3 do not add up to the total number of types as some of the higher level types are not present on their own, such as other.

  11. 11.

    dbpedia: is shorthand for http://dbpedia.org/resource.

  12. 12.

    dbo: is shorthand for http://dbpedia.org/ontology/.

  13. 13.

    http://www.mpi-inf.mpg.de/departments/databases-and-information-systems/research/yago-naga/yago/downloads/.

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Acknowledgements

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

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Authors and Affiliations

  1. Computational Lexicology and Terminology Lab, The Network Institute, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands

    Marieke van Erp & Piek Vossen

Authors
  1. Marieke van Erp
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  2. Piek Vossen
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Corresponding author

Correspondence to Marieke van Erp .

Editor information

Editors and Affiliations

  1. Universidad Politécnica de Madrid, Madrid, Spain

    Jorge Gracia

  2. Nanyang Technological University, Singapore, Singapore

    Francis Bond

  3. Insight Centre for Data Analytics, National University of Ireland, Galway, Galway, Ireland

    John P. McCrae

  4. Insight Centre for Data Analytics, National University of Ireland, Galway, Ireland

    Paul Buitelaar

  5. Goethe-University Frankfurt, Frankfurt, Germany

    Christian Chiarcos

  6. University of Leipzig, Leipzig, Germany

    Sebastian Hellmann

Appendix A: Results

Appendix A: Results

Table 5. Precision, recall and F\(_{1}\) scores on the overall datasets (macro-averaged) and per class.
Full size table

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van Erp, M., Vossen, P. (2017). Multilingual Fine-Grained Entity Typing. In: Gracia, J., Bond, F., McCrae, J., Buitelaar, P., Chiarcos, C., Hellmann, S. (eds) Language, Data, and Knowledge. LDK 2017. Lecture Notes in Computer Science(), vol 10318. Springer, Cham. https://doi.org/10.1007/978-3-319-59888-8_23

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  • DOI: https://doi.org/10.1007/978-3-319-59888-8_23

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