Framing Named Entity Linking Error Types
Adrian Brașoveanu, Giuseppe Rizzo, Philipp Kuntschik, Albert Weichselbraun, Lyndon J. B. Nixon · 2018
Named Entity Linking (NEL) and relation extraction forms the backbone of Knowledge Base Population tasks. The recent rise of large open source KnowledgeBases 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.