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NERetrieve: Dataset for Next Generation Named Entity Recognition and Retrieval

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: 🎉🎉🎉 NERetrieve paper was accepted to Findings of EMNLP 2023 and will be presented during the BlackboxNLP 2023 poster session.🎉🎉🎉

NERetrieve Dataset

NERetrieve dataset is available in three distinct formats, catering to various use cases and research requirements:

1. Exhaustive Typed-Entity Mention Retrieval

2. Fine-grained Supervised NER

3. Zero-shot Fine-grained NER

For detailed information including data specifics, code, and results, please refer to the dedicated pages.

Citation

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",
}

License

The NERetrieve dataset is distributed under the CC BY-SA 4.0 license.

Connection

Feel free to connect with us on any issue, suggestions or just a friendly conversation.

urikacid@gmail.com