Where on Earth Do Users Say They Are?: Geo-Entity Linking for Noisy Multilingual User Input

• URL: http://arxiv.org/abs/2404.18784v1
• Date: Mon, 29 Apr 2024 15:18:33 GMT
• Title: Where on Earth Do Users Say They Are?: Geo-Entity Linking for Noisy Multilingual User Input
• Authors: Tessa Masis, Brendan O'Connor,
• Abstract summary: We present a method which represents real-world locations as averaged embeddings from labeled user-input location names. We show that our approach improves geo-entity linking on a global and multilingual social media dataset.
• Score: 2.516307239032451
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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.

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