Paper Info
Title
Same but Different: Distant Supervision for Predicting and Understanding Entity Linking Difficulty.
Abstract
Entity Linking (EL) is the task of automatically identifying entity mentions in a piece of text and resolving them to a corresponding entity in a reference knowledge base like Wikipedia. There is a large number of EL tools available for different types of documents and domains, yet EL remains a challenging task where the lack of precision on particularly ambiguous mentions often spoils the usefulness of automated disambiguation results in real applications. A priori approximations of the difficulty to link a particular entity mention can facilitate flagging of critical cases as part of semi-automated EL systems, while detecting latent factors that affect the EL performance, like corpus-specific features, can provide insights on how to improve a system based on the special characteristics of the underlying corpus. In this paper, we first introduce a consensus-based method to generate difficulty labels for entity mentions on arbitrary corpora. The difficulty labels are then exploited as training data for a supervised classification task able to predict the EL difficulty of entity mentions using a variety of features. Experiments over a corpus of news articles show that EL difficulty can be estimated with high accuracy, revealing also latent features that affect EL performance. Finally, evaluation results demonstrate the effectiveness of the proposed method to inform semi-automated EL pipelines.
Year
DOI
Venue
2019
10.1145/3297280.3297381
Proceedings of the 34th ACM/SIGAPP Symposium on Applied Computing
Keywords
DocType
Volume
distant supervision, entity linking, named entity recognition and disambiguation, supervised classification
Conference
abs/1812.10387
ISBN
Citations 
PageRank 
978-1-4503-5933-7
0
0.34
References 
Authors
0
3
Name
Order
Citations
PageRank
Renato Stoffalette João101.01
Pavlos Fafalios215419.76
Stefan Dietze359768.07