mlsanityData to 14 Oct 2026

ELEVANT: A Fully Automatic Fine-Grained Entity Linking Evaluation and Analysis Tool

arXiv 2208.07193 ↗First posted 15 Aug 2022cs.CLcited by 4

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Abstract (arXiv)

We present Elevant, a tool for the fully automatic fine-grained evaluation of a set of entity linkers on a set of benchmarks. Elevant provides an automatic breakdown of the performance by various error categories and by entity type. Elevant also provides a rich and compact, yet very intuitive and self-explanatory visualization of the results of a linker on a benchmark in comparison to the ground truth. A live demo, the link to the complete code base on GitHub and a link to a demo video are provided under https://elevant.cs.uni-freiburg.de .

Key facts. Cited by 4 papers in the map (data through 14 Oct 2026). Builds on 3 of its 22 references that are in the map. First posted on arXiv 15 Aug 2022.

Builds on

3 of 22 references.

References · 3

  1. 2020Autoregressive Entity Retrievalcited by 334
  2. 2014Context-Dependent Fine-Grained Entity Type Taggingcited by 30
  3. 2021Highly Parallel Autoregressive Entity Linking with Discriminative Correctioncited by 21

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Cited by year (2026 to 14 Oct)

Citing papers per year: 2023: 1; 2024: 1; 2025: 0; 2026: 2.

4 papers.

  1. 2024OneGen: Efficient One-Pass Unified Generation and Retrieval for LLMscited by 14
  2. 2026LELA: an LLM-based Entity Linking Approach with Zero-Shot Domain Adaptationcited by 6
  3. 2023A Fair and In-Depth Evaluation of Existing End-to-End Entity Linking Systemscited by 2
  4. 2026LELA: An End-to-end LLM-based Entity Linking Framework with Zero-shot Domain Adaptationcited by 1