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

Tessa Masis; Brendan O'Connor

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arXiv · 2404.18784

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

Tessa Masis·Brendan O'Connor

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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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Tessa Masis, Brendan O'Connor. 2024-04-29. Where on Earth Do Users Say They Are?: Geo-Entity Linking for Noisy Multilingual User Input. https://arxiv.org/abs/2404.18784

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