OmniNER2025: Diverse and Comprehensive Fine-Grained NER Dataset and Benchmark for Chinese.
Yong Zhou|Shuaipeng Liu|Yunqing Li|Mengting Hu|Wen Dai|Xiaowei Zhao|Xiujuan Xu
| Anthology ID: | DBLP:conf/sigir/ZhouLLHD0X25 |
|---|---|
| Volume: | Proceedings of the 48th International ACM SIGIR Conference on Research and Development in Information Retrieval, SIGIR 2025, Padua, Italy, July 13-18, 2025 |
| Year: | 2025 |
| Venue: | Annual International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR) |
| Publisher: | ACM |
| Pages: | 85-94 |
| URL: | https://doi.org/10.1145/3726302.3730048 |
| DOI: | https://doi.org/10.1145/3726302.3730048 |
| DBLP: | conf/sigir/ZhouLLHD0X25 |
| BibTeX: |
@inproceedings{zhou-2025-omniner2025,
author = {Yong Zhou and
Shuaipeng Liu and
Yunqing Li and
Mengting Hu and
Wen Dai and
Xiaowei Zhao and
Xiujuan Xu},
editor = {Nicola Ferro and
Maria Maistro and
Gabriella Pasi and
Omar Alonso and
Andrew Trotman and
Suzan Verberne},
title = {{OmniNER2025: Diverse and Comprehensive Fine-Grained NER Dataset and Benchmark for Chinese}},
booktitle = {{Proceedings of the 48th International ACM SIGIR Conference on Research and Development in Information Retrieval, SIGIR 2025, Padua, Italy, July 13-18, 2025}},
pages = {85--94},
publisher = {ACM},
year = {2025},
url = {https://doi.org/10.1145/3726302.3730048},
doi = {https://doi.org/10.1145/3726302.3730048}
}