Work

Framing Named Entity Linking Error Types

Year: 2018

Type: conference-paper

Abstract: 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.

Source: Language Resources and Evaluation Conference (LREC)

DOI: 10.63317/52h56z532s75

Authors: Adrian Braşoveanu, Giuseppe Rizzo, Philipp Kuntschik, Albert Weichselbraun, Lyndon J.B. Nixon

Institutions: Transilvania University of Brașov, Istituto Superiore Mario Boella, University of Applied Sciences of the Grisons, MODUL University Vienna

Language: English

Topic: Topic Modeling

Subfield: Artificial Intelligence

Field: Computer Science

Domain: Physical Sciences

Open Access status: hybrid

Funder: European Commission

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