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Entity Linking in Queries: Efficiency vs. Effectiveness

  • Faegheh HasibiEmail author
  • Krisztian Balog
  • Svein Erik Bratsberg
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10193)

Abstract

Identifying and disambiguating entity references in queries is one of the core enabling components for semantic search. While there is a large body of work on entity linking in documents, entity linking in queries poses new challenges due to the limited context the query provides coupled with the efficiency requirements of an online setting. Our goal is to gain a deeper understanding of how to approach entity linking in queries, with a special focus on how to strike a balance between effectiveness and efficiency. We divide the task of entity linking in queries to two main steps: candidate entity ranking and disambiguation, and explore both unsupervised and supervised alternatives for each step. Our main finding is that best overall performance (in terms of efficiency and effectiveness) can be achieved by employing supervised learning for the entity ranking step, while tackling disambiguation with a simple unsupervised algorithm. Using the Entity Recognition and Disambiguation Challenge platform, we further demonstrate that our recommended method achieves state-of-the-art performance.

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Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  • Faegheh Hasibi
    • 1
    Email author
  • Krisztian Balog
    • 2
  • Svein Erik Bratsberg
    • 1
  1. 1.Norwegian University of Science and TechnologyTrondheimNorway
  2. 2.University of StavangerStavangerNorway

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