Abstract
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 .- Anthology ID:
- 2022.emnlp-demos.8
- Volume:
- Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing: System Demonstrations
- Month:
- December
- Year:
- 2022
- Address:
- Abu Dhabi, UAE
- Editors:
- Wanxiang Che, Ekaterina Shutova
- Venue:
- EMNLP
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 72–79
- Language:
- URL:
- https://aclanthology.org/2022.emnlp-demos.8
- DOI:
- 10.18653/v1/2022.emnlp-demos.8
- Cite (ACL):
- Hannah Bast, Matthias Hertel, and Natalie Prange. 2022. ELEVANT: A Fully Automatic Fine-Grained Entity Linking Evaluation and Analysis Tool. In Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing: System Demonstrations, pages 72–79, Abu Dhabi, UAE. Association for Computational Linguistics.
- Cite (Informal):
- ELEVANT: A Fully Automatic Fine-Grained Entity Linking Evaluation and Analysis Tool (Bast et al., EMNLP 2022)
- PDF:
- https://preview.aclanthology.org/emnlp-22-attachments/2022.emnlp-demos.8.pdf