@inproceedings{masis-oconnor-2024-earth,
    title = "Where on Earth Do Users Say They Are?: Geo-Entity Linking for Noisy Multilingual User Input",
    author = "Masis, Tessa  and
      O{'}Connor, Brendan",
    editor = "Card, Dallas  and
      Field, Anjalie  and
      Hovy, Dirk  and
      Keith, Katherine",
    booktitle = "Proceedings of the Sixth Workshop on Natural Language Processing and Computational Social Science (NLP+CSS 2024)",
    month = jun,
    year = "2024",
    address = "Mexico City, Mexico",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/jlcl-multiple-ingestion/2024.nlpcss-1.7/",
    doi = "10.18653/v1/2024.nlpcss-1.7",
    pages = "86--98",
    abstract = "Geo-entity linking is the task of linking a location mention to the real-world geographic location. In this 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."
}