Where on Earth Do Users Say They Are?: Geo-Entity Linking for Noisy Multilingual User Input
<|reference_start|>Where on Earth Do Users Say They Are?: Geo-Entity Linking for Noisy Multilingual User Input: Geo-entity linking is the task of linking a location mention to the real-world geographic location. In this paper we explore the challenging task of geo-entity linking for noisy, multilingual social media data. There are few open-source multilingual geo-entity linking tools available and existing ones are often rule-based, which break easily in social media settings, or LLM-based, which are too expensive for large-scale datasets. We present a method which represents real-world locations as averaged embeddings from labeled user-input location names and allows for selective prediction via an interpretable confidence score. We show that our approach improves geo-entity linking on a global and multilingual social media dataset, and discuss progress and problems with evaluating at different geographic granularities.<|reference_end|>
@article{masis2024where,
    title={Where on Earth Do Users Say They Are?: Geo-Entity Linking for Noisy
  Multilingual User Input},
    author={Tessa Masis, Brendan O'Connor},
    journal={arXiv preprint arXiv:2404.18784},
    year={2024},
    archivePrefix={arXiv},
    eprint={2404.18784},
    primaryClass={cs.CL cs.AI}
}