Published May 9, 2018 | Version v1
Conference paper Open

Framing Named Entity Linking Error Types

  • 1. MODUL Technology GmbH, Vienna, Austria
  • 2. ISMB, Via Pier Carlo Boggio 61, 10138 Torino, Italy
  • 3. Swiss Institute for Information Research, University of Applied Sciences Chur


Named Entity Linking (NEL) and relation extraction forms the backbone of Knowledge Base Population tasks. The recent rise of large open source Knowledge Bases and the continuous focus on improving NEL performance has led to the creation of automated benchmark solutions during the last decade. The benchmarking of NEL systems offers a valuable approach to understand a NEL system’s performance quantitatively. However, an in-depth qualitative analysis that helps improving NEL methods by identifying error causes usually requires a more thorough error analysis. This paper proposes a taxonomy to frame common errors and applies this taxonomy in a survey study to assess the performance of four well-known Named Entity Linking systems on three recent gold standards.



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InVID – In Video Veritas – Verification of Social Media Video Content for the News Industry 687786
European Commission