  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.
