GeoGLUE：一个地理语言理解评估基准
GeoGLUE: A GeoGraphic Language Understanding Evaluation Benchmark
AI总结：
针对地理NLP缺乏统一基准的问题，提出GeoGLUE基准，包含六个地理语言理解任务，实验证明其有效性与重要性。
摘要：
随着地理应用的快速发展，设计自动化且智能的模型以处理大量信息至关重要。然而，很少有研究者关注地理自然语言处理，也从未有过一个基准来建立统一标准。在这项工作中，我们提出了一个名为GeoGLUE的地理语言理解评估基准。我们从公开可用的地理资源中收集数据，并引入了六个自然语言理解任务，包括地理文本相似性召回、地理文本相似性重排序、地理元素标注、地理构成分析、地理何处何物切分以及地理实体对齐。我们还提供了评估实验和通用基线的分析，表明了GeoGLUE基准的有效性和重要性。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
