With a fast developing pace of geographic applications, automatable and intelligent models are essential to be designed to handle the large volume of information. However, few researchers focus on geographic natural language processing, and there has never been a benchmark to build a unified standard. In this work, we propose a GeoGraphic Language Understanding Evaluation benchmark, named GeoGLUE. We collect data from open-released geographic resources and introduce six natural language understanding tasks, including geographic textual similarity on recall, geographic textual similarity on rerank, geographic elements tagging, geographic composition analysis, geographic where what cut, and geographic entity alignment. We also pro vide evaluation experiments and analysis of general baselines, indicating the effectiveness and significance of the GeoGLUE benchmark.

翻译：随着地理应用的快速发展，亟需设计自动化和智能化模型来处理海量信息。然而，目前鲜有研究者专注于地理自然语言处理领域，且尚未形成统一标准的基准测试。本研究提出地理语言理解评估基准（GeoGLUE），通过收集开源地理资源数据，构建了六项自然语言理解任务，包括：地理文本召回相似度、地理文本重排序相似度、地理要素标注、地理成分分析、地理时空切分及地理实体对齐。此外，我们提供了基线模型的评估实验与分析，验证了GeoGLUE基准的有效性与重要价值。