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On the Long-Tail Entities in News

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Advances in Information Retrieval (ECIR 2017)

Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 10193))

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Abstract

Long-tail entities represent unique challenges for state-of-the-art entity linking systems since they are under-represented in general knowledge bases. This paper studies long-tail entities in news corpora. We conduct experiments on a large news collection of one million articles, where we devise an approach for measuring the volume of such entities in news and we uncover insights on the challenges associated with linking these entities to general knowledge bases.

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Notes

  1. 1.

    https://github.com/dbpedia-spotlight/.

  2. 2.

    https://github.com/dbpedia-spotlight/dbpedia-spotlight/wiki/Web-service.

  3. 3.

    http://mappings.dbpedia.org/server/ontology/classes/.

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Author information

Authors and Affiliations

  1. School of Computer Science and Electronic Engineering, University of Essex, Colchester, UK

    José Esquivel

  2. Signal Media Ltd., 32-38 Leman Street, London, E1 8EW, UK

    José Esquivel, Dyaa Albakour, Miguel Martinez, David Corney & Samir Moussa

Authors
  1. José Esquivel
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  2. Dyaa Albakour
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  3. Miguel Martinez
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  4. David Corney
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  5. Samir Moussa
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Corresponding author

Correspondence to Dyaa Albakour .

Editor information

Editors and Affiliations

  1. University of Glasgow , Glasgow, United Kingdom

    Joemon M Jose

  2. TU Delft - EWI/ST/WIS , Delft, The Netherlands

    Claudia Hauff

  3. Middle East Technical University , Ankara, Turkey

    Ismail Sengor Altıngovde

  4. Open University , Milton Keynes, United Kingdom

    Dawei Song

  5. Signal Media , London, United Kingdom

    Dyaa Albakour

  6. Toronto, Canada

    Stuart Watt

  7. JohnTait.net Ltd. and BCS IRSG , Sunderland, United Kingdom

    John Tait

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Esquivel, J., Albakour, D., Martinez, M., Corney, D., Moussa, S. (2017). On the Long-Tail Entities in News. In: Jose, J., et al. Advances in Information Retrieval. ECIR 2017. Lecture Notes in Computer Science(), vol 10193. Springer, Cham. https://doi.org/10.1007/978-3-319-56608-5_67

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  • DOI: https://doi.org/10.1007/978-3-319-56608-5_67

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-56607-8

  • Online ISBN: 978-3-319-56608-5

  • eBook Packages: Computer ScienceComputer Science (R0)

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