A machine learning approach to extracting spatial information from geological texts in Chinese. Issue 11 (2nd November 2022)

• Record Type: Journal Article
• Authors: Chu, Deping; Wan, Bo; Li, Hong; Dong, Shuai; Fu, Jinming; Liu, Yiyang; Huang, Kuan; Liu, Hui
• Is Part Of: International journal of geographical information science. Volume 36:Issue 11(2022)
• Page Start: 2169
• Page End: 2193
• Publication Date: 2022-11-02
• Subjects: Spatial information extraction -- underground spatial -- natural language processing -- fine-grained -- Chinese geological texts; Geography -- Data processing -- Periodicals; Information storage and retrieval systems -- Periodicals; Géomatique -- Périodiques; Systèmes d'information -- Périodiques; 910.285
• DOI: 10.1080/13658816.2022.2087224
• Languages: English
• ISSNs: 1365-8816

Abstract

Abstract: Texts have become an important spatial data resource. Interpretation of unstructured geoscience texts using natural language processing methods can effectively facilitate the discovery and retrieval of geographic information. Yet studies on the extraction of spatial information from textual geoscience data are limited compared to digital geoscience data. In this work, a machine learning approach is proposed for mining spatial relations in Chinese geological texts. The approach views spatial relation extraction as a sequence labeling problem, avoids the division of relation categories, and enables mining fine-grained spatial relations. The extracted geological texts commonly describe three-dimensional spatial relations among regions, strata, and lithologies. The extracted spatial relations are classified into three major categories (topological relations, absolute directional relations and relative directional relations) and 14 subcategories. We validated the proposed model with a test dataset, constructed visual displays of the extracted spatial relations on different topics, and quantified the uncertainty in the process from spatial entity recognition to spatial relation extraction. With the detailed portrayal of these spatial relations, this study provides support for solving theoretical and practical problems of cognition, prediction, decision-making, and evaluation in geoscience.