A Transformer-based Framework for POI-level Social Post Geolocation
| Autor: | Li, Menglin, Lim, Kwan Hui, Guo, Teng, Liu, Junhua |
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| Rok vydání: | 2022 |
| Předmět: | |
| Druh dokumentu: | Working Paper |
| Popis: | POI-level geo-information of social posts is critical to many location-based applications and services. However, the multi-modality, complexity and diverse nature of social media data and their platforms limit the performance of inferring such fine-grained locations and their subsequent applications. To address this issue, we present a transformer-based general framework, which builds upon pre-trained language models and considers non-textual data, for social post geolocation at the POI level. To this end, inputs are categorized to handle different social data, and an optimal combination strategy is provided for feature representations. Moreover, a uniform representation of hierarchy is proposed to learn temporal information, and a concatenated version of encodings is employed to capture feature-wise positions better. Experimental results on various social datasets demonstrate that three variants of our proposed framework outperform multiple state-of-art baselines by a large margin in terms of accuracy and distance error metrics. Comment: Full papers are 12 pages in length plus additional 4 pages for references (turns to 18 pages in total after submitting to arxiv). One figure and 5 tables are contained. This paper was submitted to ECIR 2023 for review |
| Databáze: | arXiv |
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