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https://github.com/katzurik/NERetrieve


https://github.com/katzurik/NERetrieve

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README

        

# 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**](https://arxiv.org/abs/2310.14282).

\*\*\*\*\*\*\*\*\*\*\*\*\*\*\*\*\*\*\*\*\*\*\*\*\*\*\*\*\*\*\*\*\*\*\*\*\*\*\*\*\*\*\*\*\*\*\*\*\*\* **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**](/Retrieval/IR_readme.md)

2. [**Fine-grained Supervised NER**](/supervised_NER/sup_readme.md)

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:

```bibtex
@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.

[[email protected]](mailto:[email protected])