This is the original code and dataset of the NERretrieve paper: NERetrieve: Dataset for Next Generation Named Entity Recognition and Retrieval.
************************************************** Updates **************************************************
- 03/12/2023: πππ NERetrieve paper was accepted to Findings of EMNLP 2023 and will be presented during the BlackboxNLP 2023 poster session.πππ
NERetrieve dataset is available in three distinct formats, catering to various use cases and research requirements:
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Zero-shot Fine-grained NER
For detailed information including data specifics, code, and results, please refer to the dedicated pages.
Please Cite our work using:
@inproceedings{katz-etal-2023-neretrieve,
title = "{NER}etrieve: Dataset for Next Generation Named Entity Recognition and Retrieval",
author = "Katz, Uri and
Vetzler, Matan and
Cohen, Amir and
Goldberg, Yoav",
editor = "Bouamor, Houda and
Pino, Juan and
Bali, Kalika",
booktitle = "Findings of the Association for Computational Linguistics: EMNLP 2023",
month = dec,
year = "2023",
address = "Singapore",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2023.findings-emnlp.218",
pages = "3340--3354",
}
The NERetrieve dataset is distributed under the CC BY-SA 4.0 license.
Feel free to connect with us on any issue, suggestions or just a friendly conversation.