Year: 2023
Type: preprint
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.
Source: arXiv (Cornell University)
Authors: Li, Dongyang, Ding, Ruixue, Zhang, Qiang, Li, Zheng, Chen, Boli, Xie, Pengjun, Xu, Yao, Li, Xin, Guo, Ning, Huang, Fei, He, Xiaofeng
Institutions: East China Normal University, Alibaba Group (China)
Language: English
Topic: Topic Modeling
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: green
OpenAlex ID: W4376312075