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        <title>Where on Earth Do Users Say They Are?: Geo-Entity Linking for Noisy Multilingual User Input</title>
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    <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.</abstract>
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%0 Conference Proceedings
%T Where on Earth Do Users Say They Are?: Geo-Entity Linking for Noisy Multilingual User Input
%A Masis, Tessa
%A O’Connor, Brendan
%Y Card, Dallas
%Y Field, Anjalie
%Y Hovy, Dirk
%Y Keith, Katherine
%S Proceedings of the Sixth Workshop on Natural Language Processing and Computational Social Science (NLP+CSS 2024)
%D 2024
%8 June
%I Association for Computational Linguistics
%C Mexico City, Mexico
%F masis-oconnor-2024-earth
%X 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.
%R 10.18653/v1/2024.nlpcss-1.7
%U https://aclanthology.org/2024.nlpcss-1.7
%U https://doi.org/10.18653/v1/2024.nlpcss-1.7
%P 86-98

Markdown (Informal)

[Where on Earth Do Users Say They Are?: Geo-Entity Linking for Noisy Multilingual User Input](https://aclanthology.org/2024.nlpcss-1.7) (Masis & O’Connor, NLP+CSS-WS 2024)

• Where on Earth Do Users Say They Are?: Geo-Entity Linking for Noisy Multilingual User Input (Masis & O’Connor, NLP+CSS-WS 2024)

ACL

• Tessa Masis and Brendan O’Connor. 2024. Where on Earth Do Users Say They Are?: Geo-Entity Linking for Noisy Multilingual User Input. In Proceedings of the Sixth Workshop on Natural Language Processing and Computational Social Science (NLP+CSS 2024), pages 86–98, Mexico City, Mexico. Association for Computational Linguistics.