Scholarlink

With the rapid growth 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 provide evaluation experiments and analysis of general baselines, indicating the effectiveness and significance of the GeoGLUE benchmark ( https://modelscope.cn/datasets/iic/GeoGLUE/summary ).

错误：搜索内容不能为空，请输入英文关键词
错误：关键词超出字数限制，请精简
高级检索

GeoGLUE: A Chinese GeoGraphic Language Understanding Evaluation Benchmark

  • 会议论文

  • 发表日期：

    2025年

查看全文
  • Dongyang Li,
  • Ruixue Ding,
  • Qiang Zhang,
  • Zheng Li,
  • Boli Chen,
  • Pengjun Xie,
  • Yao Xu,
  • Xin Li,
  • Ning Guo,
  • Fei Huang,
  • Xiaofeng He

摘要

With the rapid growth 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 provide evaluation experiments and analysis of general baselines, indicating the effectiveness and significance of the GeoGLUE benchmark ( https://modelscope.cn/datasets/iic/GeoGLUE/summary ).