![Sample our Geography journals, sign in here to start your FREE access for 14 days]({"type":"image" , "src" : "/sda/5937/TOC-Subject-Sample-2021-Geography.jpg"})
ABSTRACT
Spatial relation extraction is the process of identifying geographic entities from text and determining their corresponding spatial relations. Traditional spatial relation extraction mainly uses rule-based pattern matching, supervised learning-based or unsupervised learning-based methods. However, these methods suffer from poor time-sensitive, high labor cost and high dependence on large-scale data. With the development of pre-trained language models greatly alleviating the shortcomings of traditional methods, supervised learning methods incorporating pre-trained language models have become the mainstream relation extraction methods. Pipeline extraction and joint extraction, as the two most dominant ideas of relation extraction, both have obtained good performance on different datasets, and whether to share the contextual information of entities and relations is the main differences between the two ideas. In this paper, we compare the performance of two ideas oriented to spatial relation extraction based on Chinese corpus data in the field of geography and verify which method based on pre-trained language models is more suitable for Chinese spatial relation extraction. We fine-tuned the hyperparameters of the two models to optimize the extraction accuracy before the comparison experiments. The results of the comparison experiments show that pipeline extraction performs better than joint extraction of spatial relation extraction for Chinese text data with sentence granularity, because different tasks have different focus on contextual information, and it is difficult to take account into the needs of both tasks by sharing contextual information. In addition, we further compare the performance of the two models with the rule-based template approach in extracting topological, directional and distance relations, summarize the shortcomings of this experiment and provide an outlook for future work.
Acknowledgments
The authors thank Teng Zhong, Yanxu Lu and Shu Wang for their constructive comments.
Disclosure statement
No potential conflict of interest was reported by the author(s).
Data availability statement
The data that support the findings of this study are available from the corresponding author upon reasonable request.
Additional information
Funding
Notes on contributors
Kehan Wu
Kehan Wu received the bachelor’s degree in geographic information science from the Zhejiang A&F University, China, in 2019. He is currently a masters student at Nanjing Normal University. His current research interest is geographic big data mining and GeoAI.
Xueying Zhang
XueYing Zhang is currently a professor and doctoral supervisor of the School of Geographical Sciences of Nanjing Normal University. Her research fields include geographic big data, geographic knowledge map, smart city and humanistic social GIS. She presided over and participated in the 863 Program, the National Key Research and Development Program, and the National Natural Science Foundation of China.
Yulong Dang
Yulong Dang received the bachelor’s degree in geographic information science from the Chang’an University, China, in 2021. He is currently a masters student at Nanjing Normal University. His current research interest is geographic big data mining and knowledge graph.
Peng Ye
Peng Ye received the PhD degree in Cartography and Geographical Information System from Nanjing Normal University, in 2021. He is currently a research assistant professor with the Urban Planning and Development Institute, Yangzhou University. As a member of Smart City Group, his current research interest is geographic big data mining.