title: ExtEnD
emoji: 🚀
colorFrom: green
colorTo: gray
sdk: streamlit
sdk_version: 1.2.0
app_file: app.py
pinned: false
license: cc-by-nc-sa-4.0
ExtEnD: Extractive Entity Disambiguation
This space contains the demo of ExtEnD: Extractive Entity Disambiguation, a novel approach to Entity Disambiguation (i.e. the task of linking a mention in context with its most suitable entity in a reference knowledge base) where we reformulate this task as a text extraction problem. This work was accepted at ACL 2022.
If you find this demo, our paper, code or framework useful, please reference this work in your paper:
@inproceedings{barba-etal-2021-consec,
title = "{E}xt{E}n{D}: Extractive Entity Disambiguation",
author = "Barba, Edoardo and
Procopio, Luigi and
Navigli, Roberto",
booktitle = "Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics",
month = may,
year = "2022",
address = "Online and Dublin, Ireland",
publisher = "Association for Computational Linguistics",
}
Acknowledgments
The authors gratefully acknowledge the support of the ERC Consolidator Grant MOUSSE No. 726487 under the European Union’s Horizon 2020 research and innovation programme.
This work was supported in part by the MIUR under grant “Dipartimenti di eccellenza 2018-2022” of the Department of Computer Science of the Sapienza University of Rome.
License
This work is under the Attribution-NonCommercial-ShareAlike 4.0 International (CC BY-NC-SA 4.0) license.