Abstract
Many systems rely on the ability to effectively search through databases of personal and organization entity names in multiple writing scripts. Despite this, there is a relative lack of research studying this problem in isolation. In this work, we discuss this problem in detail and support future research by publishing what we believe is the first comprehensive dataset designed for this task. Additionally, we present a number of baselines against which future work can be compared; among which, we describe a neural solution based on ByT5 (Xue et al. 2022) which demonstrates up to a 12% performance gain over preexisting baselines, indicating that there remains much room for improvement in this space.- Anthology ID:
- 2024.lrec-main.838
- Volume:
- Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024)
- Month:
- May
- Year:
- 2024
- Address:
- Torino, Italia
- Editors:
- Nicoletta Calzolari, Min-Yen Kan, Veronique Hoste, Alessandro Lenci, Sakriani Sakti, Nianwen Xue
- Venues:
- LREC | COLING
- SIG:
- Publisher:
- ELRA and ICCL
- Note:
- Pages:
- 9589–9603
- Language:
- URL:
- https://aclanthology.org/2024.lrec-main.838
- DOI:
- Cite (ACL):
- Philip Blair and Kfir Bar. 2024. JRC-Names-Retrieval: A Standardized Benchmark for Name Search. In Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024), pages 9589–9603, Torino, Italia. ELRA and ICCL.
- Cite (Informal):
- JRC-Names-Retrieval: A Standardized Benchmark for Name Search (Blair & Bar, LREC-COLING 2024)
- PDF:
- https://preview.aclanthology.org/dois-2013-emnlp/2024.lrec-main.838.pdf