GeoGLUE: A GeoGraphic Language Understanding Evaluation Benchmark

• URL: http://arxiv.org/abs/2305.06545v1
• Date: Thu, 11 May 2023 03:21:56 GMT
• Title: GeoGLUE: A GeoGraphic Language Understanding Evaluation Benchmark
• Authors: Dongyang Li, Ruixue Ding, Qiang Zhang, Zheng Li, Boli Chen, Pengjun Xie, Yao Xu, Xin Li, Ning Guo, Fei Huang and Xiaofeng He
• Abstract summary: 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. We pro vide evaluation experiments and analysis of general baselines, indicating the effectiveness and significance of the GeoGLUE benchmark.
• Score: 56.08664336835741
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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