A Transformer-based Framework for POI-level Social Post Geolocation
- URL: http://arxiv.org/abs/2211.01336v1
- Date: Wed, 26 Oct 2022 10:30:51 GMT
- Title: A Transformer-based Framework for POI-level Social Post Geolocation
- Authors: Menglin Li, Kwan Hui Lim, Teng Guo, Junhua Liu
- Abstract summary: We present a transformer-based general framework, which builds upon pre-trained language models and considers non-textual data.
We show 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.
- Score: 4.027087283290081
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: 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.
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