论文标题
基于变压器的诗级社会邮政地理位置的框架
A Transformer-based Framework for POI-level Social Post Geolocation
论文作者
论文摘要
社会帖子的Poi Level地理信息对于许多基于位置的应用程序和服务至关重要。但是，社交媒体数据及其平台的多模式，复杂性和多样性限制了推断这种细粒度位置及其后续应用的性能。为了解决这个问题，我们提出了一个基于变压器的通用框架，该框架建立在预训练的语言模型的基础上，并考虑非文本数据，以在POI级别进行社交邮政地理位置。为此，将输入分类为处理不同的社交数据，并为功能表示提供了最佳组合策略。此外，提出了层次结构的统一表示，以学习时间信息，并采用串联版本的编码来更好地捕获特征位置。各种社交数据集的实验结果表明，在准确性和距离误差指标方面，我们提出的框架的三种变体超过了多个最先进的基线。
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.
