Unified Examination of Entity Linking in Absence of Candidate Sets
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Abstract (arXiv)
Despite remarkable strides made in the development of entity linking systems in recent years, a comprehensive comparative analysis of these systems using a unified framework is notably absent. This paper addresses this oversight by introducing a new black-box benchmark and conducting a comprehensive evaluation of all state-of-the-art entity linking methods. We use an ablation study to investigate the impact of candidate sets on the performance of entity linking. Our findings uncover exactly how much such entity linking systems depend on candidate sets, and how much this limits the general applicability of each system. We present an alternative approach to candidate sets, demonstrating that leveraging the entire in-domain candidate set can serve as a viable substitute for certain models. We show the trade-off between less restrictive candidate sets, increased inference time and memory footprint for some models.
Key facts. Cited by 3 papers in the map (data through 14 Oct 2026). Builds on 15 of its 27 references that are in the map. First posted on arXiv 17 Apr 2024.
Builds on
15 of 27 references.
References · 15
- 2018BERT: Pre-training of Deep Bidirectional Transformers for Language Understandingcited by 40,399
- 2019E-BERT: Efficient-Yet-Effective Entity Embeddings for BERTcited by 96
- 2018End-to-End Neural Entity Linkingcited by 85
- 2017Deep Joint Entity Disambiguation with Local Neural Attentioncited by 79
- 2020REL: An Entity Linker Standing on the Shoulders of Giantscited by 61
- 2020Entity Linking in 100 Languagescited by 52
- 2020Investigating Entity Knowledge in BERT with Simple Neural End-To-End Entity Linkingcited by 49
- 2021EntQA: Entity Linking as Question Answeringcited by 34
- 2019Joint Learning of Named Entity Recognition and Entity Linkingcited by 26
- 2021Highly Parallel Autoregressive Entity Linking with Discriminative Correctioncited by 21
- 2023Instructed Language Models with Retrievers Are Powerful Entity Linkerscited by 18
- 2020Empirical Evaluation of Pretraining Strategies for Supervised Entity Linkingcited by 17
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Citing papers per year: 2024: 1; 2025: 1; 2026: 1.
3 papers.