Spatially oriented convolutional neural network for spatial relation extraction from natural language texts. Issue 2 (27th December 2021)

• Record Type: Journal Article
• Authors: Qiu, Qinjun; Xie, Zhong; Ma, Kai; Chen, Zhanlong; Tao, Liufeng
• Is Part Of: Transactions in GIS. Volume 26:Issue 2(2022)
• Page Start: 839
• Page End: 866
• Publication Date: 2021-12-27
• Subjects: Geographic information systems -- Periodicals; 910.285
• DOI: 10.1111/tgis.12887
• Languages: English
• ISSNs: 1361-1682

Abstract

Abstract: Spatial relation extraction (e.g., topological relations, directional relations, and distance relations) from natural language descriptions is a fundamental but challenging task in several practical applications. Current state‐of‐the‐art methods rely on rule‐based metrics, either those specifically developed for extracting spatial relations or those integrated in methods that combine multiple metrics. However, these methods all rely on developed rules and do not effectively capture the characteristics of natural language spatial relations because the descriptions may be heterogeneous and vague and may be context sparse. In this article, we present a spatially oriented piecewise convolutional neural network (SP‐CNN) that is specifically designed with these linguistic issues in mind. Our method extends a general piecewise convolutional neural network with a set of improvements designed to tackle the task of spatial relation extraction. We also propose an automated workflow for generating training datasets by integrating new sentences with those in a knowledge base, based on string similarity and semantic similarity, and then transforming the sentences into training data. We exploit a spatially oriented channel that uses prior human knowledge to automatically match words and understand the linguistic clues to spatial relations, finally leading to an extraction decision. We present both the qualitative and quantitative performance of the proposed methodology using a large dataset collected from Wikipedia. The experimental results demonstrate that the SP‐CNN, with its supervised machine learning, can significantly outperform current state‐of‐the‐art methods on constructed datasets.