Paper Info
Title
Better together: an ensemble learner for combining the results of ready-made entity linking systems
Abstract
Entity linking (EL) is the task of automatically identifying entity mentions in text and resolving them to a corresponding entity in a reference knowledge base like Wikipedia. Throughout the past decade, a plethora of EL systems and pipelines have become available, where performance of individual systems varies heavily across corpora, languages or domains. Linking performance varies even between different mentions in the same text corpus, where, for instance, some EL approaches are better able to deal with short surface forms while others may perform better when more context information is available. To this end, we argue that performance may be optimised by exploiting results from distinct EL systems on the same corpus, thereby leveraging their individual strengths on a per-mention basis. In this paper, we introduce a supervised approach which exploits the output of multiple ready-made EL systems by predicting the correct link on a per-mention basis. Experimental results obtained on existing ground truth datasets and exploiting three state-of-the-art EL systems show the effectiveness of our approach and its capacity to significantly outperform the individual EL systems as well as a set of baseline methods.
Year
DOI
Venue
2020
10.1145/3341105.3373883
SAC '20: The 35th ACM/SIGAPP Symposium on Applied Computing Brno Czech Republic March, 2020
Keywords
DocType
ISBN
Meta Entity Linking, Entity Disambiguation, Named Entity Recognition and Disambiguation, Ensemble Learning
Conference
978-1-4503-6866-7
Citations 
PageRank 
References 
0
0.34
0
Authors
3
Name
Order
Citations
PageRank
Renato Stoffalette João101.01
Pavlos Fafalios215419.76
Stefan Dietze359768.07