This model is a BERT-based Location Mention Recognition model that is adopted from the TLLMR4CM GitHub. The model is trained using Hurricane Dorian 2019 event (training data is used for training) from IDRISI-R dataset under the Type-less LMR mode and using the random version of the data. You can download this data in BILOU format from here.

• Different variants of the model are available through HuggingFace:

  • rsuwaileh/IDRISI-LMR-HD-TB
  • rsuwaileh/IDRISI-LMR-HD-TB-partition
  • rsuwaileh/IDRISI-LMR-HD-TL

• Larger models are available at TLLMR4CM GitHub.

• Models trained on the entire IDRISI-R dataset:

  • rsuwaileh/IDRISI-LMR-EN-random-typeless
  • rsuwaileh/IDRISI-LMR-EN-random-typebased
  • rsuwaileh/IDRISI-LMR-EN-timebased-typeless
  • rsuwaileh/IDRISI-LMR-EN-timebased-typebased

To cite this model:

@article{suwaileh2022tlLMR4disaster,
    title={When a Disaster Happens, We Are Ready: Location Mention Recognition from Crisis Tweets},
    author={Suwaileh, Reem and Elsayed, Tamer and Imran, Muhammad and Sajjad, Hassan},
    journal={International Journal of Disaster Risk Reduction},
    year={2022}
}

@inproceedings{suwaileh2020tlLMR4disaster,
  title={Are We Ready for this Disaster? Towards Location Mention Recognition from Crisis Tweets},
  author={Suwaileh, Reem and Imran, Muhammad and Elsayed, Tamer and Sajjad, Hassan},
  booktitle={Proceedings of the 28th International Conference on Computational Linguistics},
  pages={6252--6263},
  year={2020}
}

To cite the IDRISI-R dataset:

  @article{rsuwaileh2022Idrisi-r,
    title={IDRISI-R: Large-scale English and Arabic Location Mention Recognition Datasets for Disaster Response over Twitter},
    author={Suwaileh, Reem and Elsayed, Tamer and Imran, Muhammad},
    journal={...},
    volume={...},
    pages={...},
    year={2022},
    publisher={...}
  }